{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised Learning (2) - Decision Trees, Random Forests\n",
    "## Machine Learning Lectures by the ESA Data Analytics Team for Operations (DATO)\n",
    "#### [José Martínez Heras](https://www.linkedin.com/in/josemartinezheras/)\n",
    "\n",
    "## Resouces\n",
    "This notebook is best followed when watched along to its corresponding [decision trees and random forest for bank deposit prediction **video**](https://dlmultimedia.esa.int/download/public/videos/2048/03/006/4803_006_AR_EN.mp4)\n",
    "\n",
    "The tutorial about Decision Trees and Random Forests can be found in the [2018-MachineLearning-Lectures-ESA **GitHub**](https://github.com/jmartinezheras/2018-MachineLearning-Lectures-ESA)\n",
    "\n",
    "## Goal of today's project\n",
    "Predict if a bank term deposit woud be (or not) subscribed "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "#Let's make this notebook reproducible \n",
    "np.random.seed(42)\n",
    "random.seed(42)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score, recall_score, f1_score, accuracy_score, precision_score\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.dummy import DummyClassifier\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "### Background\n",
    "The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.\n",
    "\n",
    "The classification goal is to predict if the client will subscribe a term deposit (variable y).\n",
    "\n",
    "This dataset has been downloaded from the UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/bank+marketing\n",
    "\n",
    "### Citation\n",
    "This dataset is public available for research. The details are described in [Moro et al., 2011]. Please include this citation if you plan to use this database:\n",
    "\n",
    "[Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.\n",
    "\n",
    "Available at: [pdf] http://hdl.handle.net/1822/14838 [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt\n",
    "\n",
    "### Attribute information\n",
    "\n",
    "Bank client data:\n",
    "  \n",
    "* age (numeric)   \n",
    "* job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\",\"student\", \"blue-collar\",\"self-employed\",\"retired\",\"technician\",\"services\") \n",
    "* marital : marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed)\n",
    "* education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\")\n",
    "* default: has credit in default? (binary: \"yes\",\"no\")\n",
    "* balance: average yearly balance, in euros (numeric) \n",
    "* housing: has housing loan? (binary: \"yes\",\"no\")\n",
    "* loan: has personal loan? (binary: \"yes\",\"no\")\n",
    "\n",
    "related with the last contact of the current campaign:\n",
    "* contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") \n",
    "* day: last contact day of the month (numeric)\n",
    "* month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n",
    "* duration: last contact duration, in seconds (numeric)\n",
    "\n",
    "other attributes:\n",
    "* campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "* pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)\n",
    "* previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "* poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")\n",
    "\n",
    "Output variable (desired target):\n",
    "* y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank = pd.read_csv('data/bank/bank-full.csv', sep=';')\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Change yes/no features to 1/0 features\n",
    "* Replace month names with numeric month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f in ['default', 'housing', 'loan', 'y']:\n",
    "    bank[f] = pd.Series(np.where(bank[f].values == 'yes', 1, 0), bank.index)\n",
    "\n",
    "months = {'jan':1, 'feb':2, 'mar':3, 'apr':4, 'may':5, 'jun':6, 'jul':7, 'aug':8, 'sep':9, 'oct':10, 'nov':11, 'dec':12}\n",
    "bank['month'] = bank['month'].apply(lambda x : months[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
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       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>2143</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education  default  balance  housing  loan  \\\n",
       "0   58    management  married   tertiary        0     2143        1     0   \n",
       "1   44    technician   single  secondary        0       29        1     0   \n",
       "2   33  entrepreneur  married  secondary        0        2        1     1   \n",
       "3   47   blue-collar  married    unknown        0     1506        1     0   \n",
       "4   33       unknown   single    unknown        0        1        0     0   \n",
       "\n",
       "   contact  day  month  duration  campaign  pdays  previous poutcome  y  \n",
       "0  unknown    5      5       261         1     -1         0  unknown  0  \n",
       "1  unknown    5      5       151         1     -1         0  unknown  0  \n",
       "2  unknown    5      5        76         1     -1         0  unknown  0  \n",
       "3  unknown    5      5        92         1     -1         0  unknown  0  \n",
       "4  unknown    5      5       198         1     -1         0  unknown  0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2869fd15ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.hist(bins=20, figsize=(20,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the probability of subscribing the deposit by every categorical feature. Plot also the count of every category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job\n",
      "===\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a0b04eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a14776d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "marital\n",
      "=======\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a0fab940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a11040f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "education\n",
      "=========\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a111b470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a11400f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default\n",
      "=======\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a1d9d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1bdeuXYstGQAAAIADYElBqaoOy1xM+s/d/afT8Denn6tlev3WNL4zyTEzhx+d5OZFxo+eZ/weuvu87t7U3ZvWrVu3lKUDAAAAsMyW8pS3SnJ+kuu7+80zu7Ym2f2ktjOSfGhm/IXT095OTHL79JO4S5M8o6oePN2M+xlJLp323VFVJ06f9cKZcwEAAACwyqxdwpyfT/JPk3y+qq6Zxl6Z5I1JLq6qM5N8Pcnzpn2XJHlWkh1J7kzyoiTp7luq6vVJrpzmva67b5m2X5Lk3Unul+TPpj8AAAAAVqFFg1J3/8/Mf5+jJDl5nvmd5KULnOuCJBfMM74tyc8tthYAAAAAVt7QU94AAAAAQFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYMiiQamqLqiqb1XV/5oZe0hVXVZVN06vD57Gq6reWlU7quq6qnrKzDFnTPNvrKozZsafWlWfn455a1XVcn9JAAAAAJbPUq5QeneSzXuMnZ3k8u7emOTy6X2SnJJk4/S3Jck7k7kAleQ1SZ6W5IQkr9kdoaY5W2aO2/OzAAAAAFhFFg1K3f2JJLfsMXxqkgun7QuTnDYz/p6e8xdJjqyqhyd5ZpLLuvuW7r41yWVJNk/7Htjdn+7uTvKemXMBAAAAsArt6z2UHtbd30iS6fWnp/H1SW6ambdzGtvb+M55xudVVVuqaltVbdu1a9c+Lh0AAACA/bHcN+We7/5HvQ/j8+ru87p7U3dvWrdu3T4uEQAAAID9sa9B6ZvTz9UyvX5rGt+Z5JiZeUcnuXmR8aPnGQcAAABgldrXoLQ1ye4ntZ2R5EMz4y+cnvZ2YpLbp5/EXZrkGVX14Olm3M9Icum0746qOnF6utsLZ84FAAAAwCq0drEJVfW+JL+Y5Kiq2pm5p7W9McnFVXVmkq8ned40/ZIkz0qyI8mdSV6UJN19S1W9PsmV07zXdffuG32/JHNPkrtfkj+b/gAAAABYpRYNSt39ggV2nTzP3E7y0gXOc0GSC+YZ35bk5xZbBwAAAACrw3LflBsAAACAg5ygBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwRFACAAAAYIigBAAAAMAQQQkAAACAIYISAAAAAEMEJQAAAACGCEoAAAAADBGUAAAAABgiKAEAAAAwZO1KLwAAAJi89kErvQLgYPTa21d6BRyEVs0VSlW1uapuqKodVXX2Sq8HAAAAgPmtiqBUVWuSvD3JKUmOS/KCqjpuZVcFAAAAwHxWRVBKckKSHd395e7+UZKLkpy6wmsCAAAAYB6r5R5K65PcNPN+Z5Kn7TmpqrYk2TK9/V5V3fATWBtwaDkqybdXehGsfvWmlV4BAPh3C0t0Tq30Crh3edRSJq2WoDTf/919j4Hu85Kcd+CXAxyqqmpbd29a6XUAACzGv1uAlbRafvK2M8kxM++PTnLzCq0FAAAAgL1YLUHpyiQbq+rYqjo8yelJtq7wmgAAAACYx6r4yVt331VVZyW5NMmaJBd09/YVXhZwaPKzWgDg3sK/W4AVU933uFURAAAAACxotfzkDQAAAIB7CUEJAAAAgCGCEgAAAABDVsVNuQFWQlX9bJJTk6xP0kluTrK1u69f0YUBAACscq5QAg5JVfV7SS5KUkk+m+TKaft9VXX2Sq4NAGBEVb1opdcAHHo85Q04JFXVF5M8obv/eo/xw5Ns7+6NK7MyAIAxVfX17n7kSq8DOLT4yRtwqPpxkkck+doe4w+f9gEArBpVdd1Cu5I87Ce5FoBEUAIOXb+T5PKqujHJTdPYI5M8JslZK7YqAID5PSzJM5Pcusd4JfnUT345wKFOUAIOSd390ap6bJITMndT7kqyM8mV3X33ii4OAOCePpzk/t19zZ47quqKn/xygEOdeygBAAAAMMRT3gAAAAAYIigBAAAAMERQAgBYoqp6bVX97l72r6uqz1TV1VX1C/tw/t+sqrdN26dV1XH7s14AgANFUAIAWD4nJ/nL7j6+uz+5n+c6LYmgBACsSoISAMBeVNWrquqGqvofSR43jT26qj5aVVdV1Ser6mer6slJ/l2SZ1XVNVV1v6p6Z1Vtq6rtVXXOzDm/WlVHTdub9nxCU1U9PcmvJPn307ke/ZP6vgAAS7F2pRcAALBaVdVTk5ye5PjM/bvpc0muSnJekhd3941V9bQk7+juk6rq1Uk2dfdZ0/Gv6u5bqmpNksur6ondfd1in9vdn6qqrUk+3N0fPEBfDwBgnwlKAAAL+4Uk/7W770ySKfIckeTpST5QVbvn3XeB4/9xVW3J3L+5Hp65n7AtGpQAAFY7QQkAYO96j/f3SXJbdz95bwdV1bFJfjfJ3+nuW6vq3ZmLUUlyV/7/rQeOmOdwAIBVzT2UAAAW9okk/2i6H9IDkvxykjuTfKWqnpckNedJ8xz7wCTfT3J7VT0sySkz+76a5KnT9q8t8Nl3JHnA/n8FAIDlJygBACyguz+X5P1JrknyJ0l2P7ntN5KcWVXXJtme5NR5jr02ydXT/guS/PnM7nOS/Ieq+mSSuxf4+IuS/JuqutpNuQGA1aa697yKGwAAAAAW5golAAAAAIYISgAAAAAMEZQAAAAAGCIoAQAAADBEUAIAAABgiKAEAAAAwBBBCQAAAIAhghIAAAAAQ/4vVGHQx2V4rQoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a1dc3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "housing\n",
      "=======\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a23f8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a2202160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan\n",
      "====\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a26186d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a25e9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contact\n",
      "=======\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a2597780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a21e1208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "=====\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a0a80cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a1492c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "poutcome\n",
      "========\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a0082438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9JsmzkpxfVecOtVdk8pa2AzJ5PO2SJP85Sbr7gqp6f5KvZvKGuOO6+6YkqarnJzktyYoka7r7guF6L01yclW9Nsk5mQRYAAAAACxBmw2UuvszWXido1Nv4ZzXJXndAvVTFzqvuy/O5C1wAAAAACxxo97yBgAAAAACJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABG2WygVFV7VdXHq+rCqrqgql4w1HetqtOr6qLh6y5DvarqLVW1rqrOq6qHTV3rmGH8RVV1zFT94VV1/nDOW6qqtsfNAgAAALDttmSG0o1Jfq+790tyUJLjqmr/JC9LckZ375vkjGE/SY5Isu/wOTbJ8ckkgEryyiSPTHJgklduDKGGMcdOnXf4tt8aAAAAANvDZgOl7r68u780bF+X5MIkeyQ5MsmJw7ATkxw1bB+Z5KSe+EKSnatq9ySHJTm9u6/q7quTnJ7k8OHYXbv7893dSU6auhYAAAAAS8yoNZSqau8kD03yxST37O7Lk0nolOQew7A9klw6ddr6oXZL9fUL1Bf6/sdW1dqqWrthw4YxrQMAAACwSLY4UKqqOyf52yQv7O5/vaWhC9R6K+o/W+w+obtXd/fqVatWba5lAAAAALaDLQqUqup2mYRJf93dHxzKVwyPq2X4euVQX59kr6nT90xy2Wbqey5QBwAAAGAJ2pK3vFWSdya5sLvfNHXolCQb39R2TJIPT9WfPbzt7aAk1w6PxJ2W5NCq2mVYjPvQJKcNx66rqoOG7/XsqWsBAAAAsMSs3IIxj0nyrCTnV9W5Q+0VSV6f5P1V9dwk30ry9OHYqUmelGRdku8neU6SdPdVVfWaJGcN417d3VcN289L8u4kd0jy0eEDAAAAwBK02UCpuz+Thdc5SpInLDC+kxx3M9dak2TNAvW1SR64uV4AAAAAmL1Rb3kDAAAAAIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKNsNlCqqjVVdWVVfWWq9qqq+nZVnTt8njR17OVVta6qvl5Vh03VDx9q66rqZVP1farqi1V1UVW9r6p2XMwbBAAAAGBxbckMpXcnOXyB+pu7+4Dhc2qSVNX+SY5O8oDhnLdX1YqqWpHkbUmOSLJ/kmcMY5PkDcO19k1ydZLnbssNAQAAALB9bTZQ6u5PJblqC693ZJKTu/uG7v5GknVJDhw+67r74u7+YZKTkxxZVZXkkCQfGM4/MclRI+8BAAAAgFvRtqyh9PyqOm94JG6XobZHkkunxqwfajdXv3uSa7r7xk3qC6qqY6tqbVWt3bBhwza0DgAAAMDW2tpA6fgk90tyQJLLk7xxqNcCY3sr6gvq7hO6e3V3r161atW4jgEAAABYFCu35qTuvmLjdlX9ZZKPDLvrk+w1NXTPJJcN2wvVv5Nk56paOcxSmh4PAAAAwBK0VTOUqmr3qd2nJtn4BrhTkhxdVbevqn2S7JvkzCRnJdl3eKPbjpks3H1Kd3eSjyf5leH8Y5J8eGt6AgAAAODWsdkZSlX13iQHJ9mtqtYneWWSg6vqgEweT7skyX9Oku6+oKren+SrSW5Mclx33zRc5/lJTkuyIsma7r5g+BYvTXJyVb02yTlJ3rlodwcAAADAoqvJJKHlZ/Xq1b127dpZtwEAAMASceH995t1Cywj+33twlm3sCRV1dndvXpz47blLW8AAAAA3AYJlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYZbOBUlWtqaorq+orU7Vdq+r0qrpo+LrLUK+qektVrauq86rqYVPnHDOMv6iqjpmqP7yqzh/OeUtV1WLfJAAAAACLZ0tmKL07yeGb1F6W5Izu3jfJGcN+khyRZN/hc2yS45NJAJXklUkemeTAJK/cGEINY46dOm/T7wUAAADAErLZQKm7P5Xkqk3KRyY5cdg+MclRU/WTeuILSXauqt2THJbk9O6+qruvTnJ6ksOHY3ft7s93dyc5aepaAAAAACxBW7uG0j27+/IkGb7eY6jvkeTSqXHrh9ot1dcvUF9QVR1bVWurau2GDRu2snUAAAAAtsViL8q90PpHvRX1BXX3Cd29urtXr1q1aitbBAAAAGBbbG2gdMXwuFqGr1cO9fVJ9poat2eSyzZT33OBOgAAAABL1NYGSqck2fimtmOSfHiq/uzhbW8HJbl2eCTutCSHVtUuw2LchyY5bTh2XVUdNLzd7dlT1wIAAABgCVq5uQFV9d4kByfZrarWZ/K2ttcneX9VPTfJt5I8fRh+apInJVmX5PtJnpMk3X1VVb0myVnDuFd398aFvp+XyZvk7pDko8MHAAAAgCVqs4FSdz/jZg49YYGxneS4m7nOmiRrFqivTfLAzfUBAAAAwNKw2ItyAwAAADDnBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjLJNgVJVXVJV51fVuVW1dqjtWlWnV9VFw9ddhnpV1Vuqal1VnVdVD5u6zjHD+Iuq6phtuyUAAAAAtqfFmKH0+O4+oLtXD/svS3JGd++b5IxhP0mOSLLv8Dk2yfHJJIBK8sokj0xyYJJXbgyhAAAAAFh6tscjb0cmOXHYPjHJUVP1k3riC0l2rqrdkxyW5PTuvqq7r05yepLDt0NfAAAAACyCbQ2UOsk/VdXZVXXsULtnd1+eJMPXewz1PZJcOnXu+qF2c/WfUVXHVtXaqlq7YcOGbWwdAAAAgK2xchvPf0x3X1ZV90hyelV97RbG1gK1voX6zxa7T0hyQpKsXr16wTEAACx9DzrxQbNugWXk/GPOn3ULAGxim2Yodfdlw9crk3wokzWQrhgeZcvw9cph+Poke02dvmeSy26hDgAAAMAStNWBUlXdqarusnE7yaFJvpLklCQb39R2TJIPD9unJHn28La3g5JcOzwSd1qSQ6tql2Ex7kOHGgAAAABL0LY88nbPJB+qqo3X+Zvu/seqOivJ+6vquUm+leTpw/hTkzwpybok30/ynCTp7quq6jVJzhrGvbq7r9qGvgAAAADYjrY6UOrui5M8ZIH6d5M8YYF6JznuZq61Jsmare0FAAAAgFvPtr7lDQAAAIDbGIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYJSVs24AgCXuVXebdQcsJ6+6dtYdAABwKzBDCQAAAIBRzFCaI3u/7B9m3QLLyCWvf/KsWwAAAGCZMkMJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoSyZQqqrDq+rrVbWuql42634AAAAAWNiSCJSqakWStyU5Isn+SZ5RVfvPtisAAAAAFrIkAqUkByZZ190Xd/cPk5yc5MgZ9wQAAADAAlbOuoHBHkkundpfn+SRmw6qqmOTHDvsfq+qvn4r9Mbyt1uS78y6iaWm3jDrDmDZ87tlIX9cs+4Alju/WxZQv+Z3C2wjv1sWUn633Iz7bMmgpRIoLfRfsX+m0H1CkhO2fzvMk6pa292rZ90HMF/8bgG2B79bgO3B7xa2h6XyyNv6JHtN7e+Z5LIZ9QIAAADALVgqgdJZSfatqn2qasckRyc5ZcY9AQAAALCAJfHIW3ffWFXPT3JakhVJ1nT3BTNui/nhMUlge/C7Bdge/G4Btge/W1h01f0zSxUBAAAAwM1aKo+8AQAAALBMCJQAAAAAGEWgBAAAAMAoAiUA2AJVtaKqXjTrPgAAYCmwKDdzqaqenuQfu/u6qvrDJA9L8tru/tKMWwOWsar6RHcfPOs+gPlRVZXkmUnu292vrqp7J7lXd58549aAZa6qbp/kaUn2ztQb3rv71bPqiflihhLz6o+GMOmxSQ5LcmKS42fcE7D8fbaq/qKqHldVD9v4mXVTwLL29iSPSvKMYf+6JG+bXTvAHPlwkiOT3Jjk+qkPLIqVmx8Cy9JNw9cnJzm+uz9cVa+aYT/AfHj08HX6X/Y6ySEz6AWYD4/s7odV1TlJ0t1XV9WOs24KmAt7dvfhs26C+SVQYl59u6r+R5InJnnDMN3TjDxgm3T342fdAzB3flRVKzIJp1NVq5L8eLYtAXPic1X1oO4+f9aNMJ/8BZt59R+SnJbk8O6+JsmuSV4y25aA5a6q7llV76yqjw77+1fVc2fdF7CsvSXJh5Lco6pel+QzSf5kti0Bc+KxSc6uqq9X1XlVdX5VnTfrppgfFuVmLlXV/ZKs7+4bqurgJA9OctIQLgFslSFIeleSP+juh1TVyiTndPeDZtwasIxV1f2TPCFJJTmjuy+ccUvAHKiq+yxU7+5v3tq9MJ8ESsylqjo3yepM3mhwWpJTkvxCdz9pln0By1tVndXdj6iqc7r7oUPt3O4+YNa9ActPVe2Q5LzufuCsewHmT1W9Osmnk3yuuy3GzaLzyBvz6sfdfWOSX07yZ939oiS7z7gnYPm7vqrunp+sdXJQkmtn2xKwXHX3j5N8uaruPetegLl0SSZvkFxbVWdW1Rur6sgZ98QcsSg38+pHVfWMJM9O8pShdrsZ9gPMh9/NZMbj/arqs0lWJfmV2bYELHO7J7mgqs7M1Ou8u/uXZtcSMA+6e02SNVV1r0zWmH1xkmOT3GWmjTE3PPLGXKqq/ZP8VpLPd/d7q2qfJL/a3a+fcWvAMjesm/QLmax18vXu/tGMWwKWsar6twvVu/uTt3YvwHypqr9Ksn+SKzJ59O0zSb40PMkB20ygxNyqqjskuXd3f33WvQDzo6oencn6bP9/lm93nzSzhgAAFlBVH0ryc0m+muSTST7V3RfPtivmiUCJuVRVT0ny35Ps2N37VNUBSV5t+jiwLarqPUnul+TcJDcN5e7u35ldV8ByVlXXZViXLcmOmTyif31333V2XQHzpKr2S3JYkhclWdHde864JeaENZSYV69KcmCSTyRJd587PPYGsC1WJ9m//WsMsEi6+6fWMqmqozL5MwzANqmqf5/kcUl+MckuST6WyaNvsCgESsyrG7v72qqarvkLILCtvpLkXkkun3UjwHzq7r+rqpfNug9gLhyR5FNJ/ry7L5t1M8wfgRLz6itV9R+TrKiqfZP8TpLPzbgnYJmqqr/PJJS+S5KvDm9jumHjcY/TAlurqn55aneHTGZC+kcwYJt193FVdc8kj6iqhyU5s7uvnHVfzA9rKDGXquqOSf4gyaFD6bQkr+3uH8yuK2C5urm3MG37QDZ0AAAINUlEQVTkbUzA1qqqd03t3pjkkiR/6S99wLaqqqdnsq7sJzJ5O+3jkrykuz8wy76YHwIlANhCVfWG7n7p5moAALNWVV9O8u82BtRVtSrJP3f3Q2bbGfNCoMRcqqrTkzy9u68Z9ndJcnJ3HzbbzoDlrKq+1N0P26R2Xnc/eFY9ActTVb01t/Bom7dHAtuqqs7v7gdN7e+Q5MvTNdgW1lBiXu22MUxKku6+uqruMcuGgOWrqp6X5L8kuV9VnTd16C6xPhuwddbOugFg7v1jVZ2W5L3D/q8mOXWG/TBnzFBiLlXV2Ume2t3fGvbvk+RDm84sANgSVXW3TF63+6dJXp/J63eT5DPdfc7MGgMAuAVV9bQkj8lkDaVPdfeHZtwSc0SgxFyqqsOTnJBk40K5v5jk2O4+bXZdActdVb0gyW8k+WAmfzA7KpPFc98608aAZaeq/qy7Xzj1Fsmf4u2RACx1AiXmVlXtluSgTP7S9/nu/s6MWwKWueFxt0d19/XD/p0y+f1iDSVglKp6eHeffXNvkfT2SGBbVdUvJ3lDkntk8neiStLdfdeZNsbcECgxl6rqqUk+1t3XDvs7Jzm4u/9utp0By1lVnZ/kEd39g2F/pyRnWdwSAFhqqmpdkqd094Wz7oX5JFBiLlXVud19wCa1c7r7obPqCVj+qup3kxyTZOP6A0cleXd3/9nsugKWs6raN5P12fZPstPGenffd2ZNAXOhqj7b3Y+ZdR/ML295Y17tsEDN/+/ANunuN1XVJ5I8NpNp48+xKDewjd6V5JVJ3pzk8Umek8nvF4Bttbaq3pfk75LcsLHY3R+cXUvMEzOUmEtVtSbJNUnelslCl7+dZJfu/rVZ9gUAMK2qzu7uh1fV+Rsfn62qT3f342bdG7C8VdW7Fih3d//6rd4Mc8mMDebVbyf5oyTvy+Rf+f4pyXEz7QgA4Gf9oKp2SHJRVT0/ybczWUAXYFv9XndfNV2oqn1m1QzzxwwlAAC4lVXVe7r7WVX1+0nenmTnJK9Jcrck/7W7vzDTBoFlr6o+m+SI7v7XYX+/JP+rux84286YFwIl5lJVfTyTR91+SncfMoN2AAB+SlV9NckRSU5JcnA2WTdp01kFAGNV1ZOT/H6SJyf5hSQnJXlmd58708aYGx55Y169eGp7pyRPS3LjjHoBANjUO5L8Y5L7Jjk7k0Cpp756yxuwTbr7H6rqdpks/3GXJEd190Uzbos5YoYStxlV9cnu/rez7gMAYKOqOr67nzfrPoD5UVVvzU8/rXFIkouTXJIk3f07M2iLOWSGEnOpqnad2t0hyeok95pROwAACxImAdvB2k32z55JF8w9M5SYS1X1jfxk2viPMknjX93dn5llXwAAADAPdph1A7CdvDTJAd29T5L3JLk+yfdn2xIAAMCto6oeU1WnV9W/VNXFVfWNqrp41n0xP8xQYi5V1Xnd/eCqemySP0nyxiSv6O5Hzrg1AACA7a6qvpbkRZk88nbTxnp3f3dmTTFXzFBiXm38hfnkJO/o7g8n2XGG/QAAANyaru3uj3b3ld393Y2fWTfF/DBDiblUVR9J8u0kT0zy8CT/N8mZ3f2QmTYGAABwK6iq1ydZkeSDSW7YWO/uL82sKeaKQIm5VFV3THJ4kvO7+6Kq2j3Jg7r7n2bcGgAAwHZXVR8fNjf+pb+SdHcfMqOWmDMrZ90AbA/d/f1MkviN+5cnuXx2HQEAANyqPrFAzYwSFo1ACQAAAObP96a2d0ry75NcOKNemEMeeQMAAIA5V1W3T3JKdx82616YD97yBgAAAPPvjknuO+smmB8eeQMAAIA5U1Xn5ydrJq1IsirJq2fXEfPGI28AAAAwZ6rqPlO7Nya5ortvnFU/zB+BEgAAAACjWEMJAAAAgFEESgAAAACMIlACAFgkVXVwVT161n0AAGxvAiUAgMVzcBKBEgAw9wRKAMBtXlXtXVVfq6oTq+q8qvpAVd2xqp5QVedU1flVtaaqbj+Mv6Sqdhu2V1fVJ6pq7yS/leRFVXVuVT2uqu5ZVR+qqi8Pn0cP5/xuVX1l+Lxwkx7+aqj/dVU9sao+W1UXVdWBw7g7Db2cNfR25Cx+ZgDAbZtACQBg4heSnNDdD07yr0l+N8m7k/xqdz8oycokz7u5k7v7kiTvSPLm7j6guz+d5C1JPtndD0nysCQXVNXDkzwnySOTHJTkN6vqocNlfj7Jnyd5cJL7J/mPSR6b5MVJXjGM+YMkH+vuRyR5fJL/VlV3WpSfAADAFhIoAQBMXNrdnx22/2eSJyT5Rnf/y1A7MckvjrzmIUmOT5Luvqm7r80kIPpQd1/f3d9L8sEkjxvGf6O7z+/uHye5IMkZ3d1Jzk+y9zDm0CQvq6pzk3wiyU5J7j2yLwCAbbJy1g0AACwRPWLsjfnJP8ztNPL71C0cu2Fq+8dT+z/OT/7cVkme1t1fH/l9AQAWjRlKAAAT966qRw3bz0jyz0n2rqqfH2rPSvLJYfuSJA8ftp82dY3rktxlav+MDI/JVdWKqrprkk8lOWpYo+lOSZ6a5NMj+jwtyW9XVQ3XfehmxgMALDqBEgDAxIVJjqmq85LsmuTNmax19L+q6vxMZgm9Yxj7x0n+vKo+neSmqWv8fZKnblyUO8kLkjx+OP/sJA/o7i9lsjbTmUm+mOSvuvucEX2+JsntkpxXVV8Z9gEAblU1eSwfAOC2a3hD20e6+4EzbgUAYFkwQwkAAACAUcxQAgAAAGAUM5QAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIzy/wCv+U7LdrtJOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a0a42e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_features = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'poutcome']\n",
    "for f in categorical_features:\n",
    "    #display(HTML('<h3>' + f + '</h3'))\n",
    "    print(f)\n",
    "    print('=' * len(f))\n",
    "    group = bank.groupby(f)['y'].mean().sort_values(ascending=False)\n",
    "    labels = group.index.tolist()\n",
    "    group.plot(kind='bar', figsize=(20,5), title='y by ' + f);\n",
    "    plt.show()\n",
    "    bank.groupby(f)['y'].count()[labels].plot(kind='bar', figsize=(20,5), title=f + ' count');\n",
    "    plt.show()\n",
    "    #display(HTML('<br>'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's now analyze the correlation to numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4UCAmsxWDUgT4+mCuq8dUYyWiWRQ13BhAhcXK4MQ4hiTGMSghFh8vA/4+Ptw/ZRQLN+ykR0SYc/rHtiPHeHzWaS8Nj1FtrXWZvhAS4E91izmN1dZaQk/TVpovfLp30kiXaCFAk81GVmEJfeKiyS5uvZius00Z2JuJ/VMBOFpc5phqYJ+S0FZ7aRkBjWiWptJS64ymhwaeelJV29DIv1dtdh7z8l3XePSC4ikDejHBWSflruUNCmg1javlE4cIY4CzTqqsrnVycn55UlQED08bC0BQgC/pibHYbBqT2UpeaQUljsj0jqMFpMZ0Y63reu9ONz29D5MH2AeG2SfK6BZsBMdCvoigdrSboECX6PEVfVMYmtyDP36+wvlaakwkI3slcvu4YQT6+aK1pqGpiRUtFht6gpP96Enhp2snbfW17Tj2TDYfyuXxWRNYvM2zo+kVFtcnBOGBAVS2KGeF2eosf/M6Ansk/sFpY3hn9VZKmz25Ofmkqq6hka1H8kiMimDz4Tw3lMhzePrTtouh3bu7aK2PtaiQ004Y1FrPA+ad/PHQu5+dX+7EOSk0VRERFEhYoD9V1joG9OjO51t3u6Q5WFTC4IQ4Csor6R8f7dyNwNvLgAIammwkR0dg05rSajNB/n5EhgQR6OuDpb6BlJhuLh2FJ8opLic6NJjIYCMms5WMXgm8+e1GlzQ7cwsZ2yeJ7BNlDE/pQVahfVpGZm4BD0wdw4pdWYQZA4gODeZocTnZJ8r4fIu9LvvERjFjcF/mf7fZ2aHGhAZxorKG/vExrRaWeqICZ1sJoMpaa28rLR6vZxWWkJ4YR355Jf3jYzjq0lYUDU1NpERHYLPZ24qPlxd+Pl7U1NajlKJ390jyylo/efAE3+05xHd7DgGQnhjH1IG92Xw4l5SYbljrG1rtGFFpqaW2wd7+s0+UMTYtiW8dN7U7cwoYl5bM1zv2My4tmR2O6WABvj7UNzbRZLMxoV8qWUUlLnNqPc13ew/z3d7DgP3J0dRBvdl8OI+UmG5Y6tpXJyt32+t0R8s6OWqvk98s+Mp5/H2TR5GZW8COnAKUUhj9fAj296O6to5+8dHONTKeZMWug6zYZf+7D0mK58r0NDYezKFX90isdQ2tBpkVFivWhgZ6dY/k8PFSruibwjeZ9juP9MQ4rhkxgBc/W+4y//7Fz5Y7///GUenU1jd65AAd7H1tTLO+dmRqAm+06GszcwoY2yfZ0df25MDJvjangDlTx7Ai097Xxjj62jOJDg2iuNJ+ozskKZ4ik+evNcstMREdEkS3oEAqLFaGp/Tkre+3uKTZlVfE6F6JHC0uZ2hyPFmFxYC9D3nkynF8sW0P2cVlzvTNB/IGpRjYM9Z5jLi0tHeQfkwpNRbQSilf4DEcU1+6gu7PPUXAkHS8wkJJWvQ+5fPfo2rJN52drYtOa82ynVncMW4YSikycwspqTYzsV8qRRVVHCwqYUdOIdePGMjPrhyHtb6BRY6Bp9HPlzvHDUNrTVVtHV845vrV1NaxZn82P5kwgiatqbTUsnj73s4s5lnZtOaDdT/wxKyJKKVYn5VNoWP3hNyScjJzC1l3IJv7J4/mpdtmYa6rZ95K+wdLoamKbUfyeP6Wmdi0jQ/WbW/1aL7l73pvzVYenj4OjX0h5dstOmBPpLVm6c4D3Dne3lZ25hRQUm1mUv9UCk0n20oBN2QM5Oczxju36wRHWxk/HK21fUvObfY25Ovtxa1jhtoH8UqRU1zONsf2ep5sV24h6Qmx/O+ds53bDZ70/C1XObeAe3f1Nu6fMgpfby925xWxK68IsE9reWTGOCb0S6Wsxsyr36wH7Ish50wdg01rCk2VLlH1h6aPpW9cNEH+fvzf3dfxn627WbvfdUFyZ9qVV0R6Yhz/c8ds6htdnwg8d/MMnvvE3n++t2Yb900Zha+XN7vzCtntqJOvf9jPT68cxxV9UyirsfDa8vVn/H1aaxZu3Ml/XTsZBeSUmFjtQfXRlp05BQxJiucvP7me+oZG5q7c4Hzvj7dfzW8/XALAW6s289D0cfh6e5GZU+DcseUnEzPw8fLi6eunAfbFo83ruSuw97XbeeLqiRiUwdnXXjdiIDmOvnbtgWwemDKaP95+Nea6euausNdToamKbdnHeOHWWdi0jQVrT/W1c6aOIc1xffzprmtZvG0P6w5k86NRg+keFozWUFZt5r212zqz+O1ic7Ttn191BQal2Hgwh6KKKmYP609uqYndeUVsOHiUeyaO5Lmbr8JSV898RzuY2D+VqJAgZg7p55x3/o9la6lvbOTRq67Ay6BQSpFVWMy6LM++XsT5UWcagDgTKRUJ/B2Yhn0i2XLgca112RkPtNOHxs+4oExeanqv+4YXF604e8LLyLM3TgdgztyFnZwTz/LGQ7fyQrPImoDf/+hKAO599cNOzolneeuR27nvtY86Oxse5d8/vQ2AO195r5Nz4lkWPPZjHnhd2kpzbz58G4/M/7Szs+FRXr3/Jjjt6qTOd/SGO922yCf58wWdUg/tiqRrrUuBOzs4L0IIIYQQQgjaOUhXSr3SxsuVwDat9RcXN0tCCCGEEEKcgbr094xvbwn9gSHAIce/dCACuF8p9bcOypsQQgghhBCXpfYuHO0FTNFaNwIopV7DPi99OrD7TAcKIYQQQghxURk8drr8RdPeSHo80PzbRo1AnNa6CfDs74gXQgghhBCii2lvJP1PwE6l1PfYV/pOAP6olDICKzsob0IIIYQQQrQiX2bkoLWer5RaCvwYOIB9qku+1toMPNmB+RNCCCGEEOKy097dXR4AHgd6ADuB0cBGYErHZU0IIYQQQog2yO4uTo8DGUCu1noyMBQo6bBcCSGEEEIIcRlr75z0Wq11rVIKpZSf1vqAUiqtQ3MmhBBCCCFEWy6D3V3aO0jPV0qFAf8BViilTEBhx2VLCCGEEEKIy1d7F47e4Pjf55RSq4BQYFmH5UoIIYQQQojTUIZLf056eyPpTlrr1R2RESGEEEIIIYTdOQ/ShRBCCCGE6FSXwT7pl/6zAiGEEEIIIboYGaQLIYQQQgjhYWS6ixBCCCGE6FpkuosQQgghhBDC3SSSLoQQQgghupbLYAvGS7+EQgghhBBCdDESSRdCCCGEEF2KkjnpQgghhBBCCHeTSLoQQgghhOhaDBJJF0IIIYQQQriZRNKFEEIIIUTXoi79OPOlX0IhhBBCCCG6GKW17ujf0eG/QAghhBBCXHQeO/E77/5H3Ta+TJj/j06pB7dMd3lx0Qp3/Jou49kbp3No/IzOzoZH6b3uGwDu/teCTs6JZ3n3Z3fywmfLOzsbHuX3P7oSgDlzF3ZyTjzLGw/dyiPzP+3sbHiUV++/CYAHXv+ok3PiWd58+DYefuOTzs6GR3l9zs08+f7izs6GR3n5rms7OwuXPZmTLoQQQgghuhQlu7sIIYQQQggh3E0i6UIIIYQQomuRbxwVQgghhBBCuJsM0oUQQgghhPAwMt1FCCGEEEJ0LYZLP8586ZdQCCGEEEKILkYi6UIIIYQQoktREkkXQgghhBBCuJtE0oUQQgghRNciWzAKIYQQQggh3E0i6UIIIYQQomuRSLoQQgghhBDC3SSSLoQQQgghuhbZ3UUIIYQQQgjhbhJJF0IIIYQQXYqSOelCCCGEEEIId5NIuhBCCCGE6Fokki6EEEIIIYRwN4mkCyGEEEKIrsUgkXQhhBBCCCGEm8kgXQghhBBCCA8j012EEEIIIUTXoi79OPOlX0IhhBBCCCG6mC4bSU+N6caM9DSUUuzIKWDDwRyX970MiutGDCQ2LARrfQOfbdlFpaUWg1JcPawfcWEhaA3f7Moit9QEwIAe3RmfloQGqmvr+M/WPVjrG9xfODeIfvqXGMeOoslUQd7dD3V2djrcXVcMZ3BiPHUNjbzx7Ubn37y5pKgI5kwdg6+3F5m5Bby/djsAGakJ3DByEHHhoTz/yTKOlpQDEBls5H/umE1RRRUAR46X8fbqLe4r1AVKjenGjMF9MSjFjqP5rG/jGrp+xCBiw+3X0KebM53X0Oxh/YkNd1xDmQec9Xn3hBEE+fvR2NQEwPvrfsBSV+/uop2TAT27c9vYoRiUYu2BbJbtPODyvrfBwH1TRpEYGU5NbT3zVm6grMYCwMwh/RjfNxmb1ny0fgd78487j1NK8cyN06kwW/nHsrUA3DtpJH1io5z9ylvfb+FYWYWbSnp++sfHcPPoISiDYkPWUZbvynJ539tg4CcTM+gZGY65tp75qzZRXmOhb1w012cMwstgoMlmY9GWXRwsKsHHy4s5U0cTGWzEpjW784r4YtueTirduRnQszu3jxtmbyv7s1m6c7/L+94GA/dPGU1ilL2tzF25gbJqMwAzh/bjir4p2LTmw3U/sDf/ON5eBn5z3VS8DQYMBgPbs4+xuEVd3D5uGOP6JvPz+Z+5rZznq3+PGG4ZY7+W1mdl801m67Zyz6SRJESGY66r481vN1FWY8Ho58uD08aQGBXBpoM5fLRhBwB+Pt781zWTnceHGwPYfCiXTzZlurVcFyotNoprMwZhUIoth3NZtfewy/teBgO3jR1Kj25hWOrqeX/tNkxmK+HGAJ68ZgolVTUA5JaaWLRlFwAPTBlNcIAfBqU4WlzO51t3obXbi9ap1GWwcLRLDtIVcNXgvixY9wNV1loemDyKg0UllDo6Q4AhSfHU1jfyr+XrGdAjhqkDe7Noy26GJccDMPfbTQT6+XDH2GG8uWozSilmpKfx2soNWOsbmDqwNxmpPVmzP7uTStmxqr5eTuVni4l55snOzkqHS0+MIyY0hCffX0xqTDfumTSS5z/9plW6n0zM4K1Vmzl8opRfzZ5MekIcu/IKKSiv4JWla7h30qhWxxRX1vDswqXuKMZFpbAPMN9ft50qSy0PTBlNVotraGhSD6wNDfzzm3UM6NGdaQP78NmWXQxL7gHA3JUbCfTz5Y5xw3jzu03O4z7fstt54+LplFLcMW44f13yPSazld/dOJ3MnEKX/I/vm4Klrp7fffQ1Gak9+dHowcxbuZHYsBAyeiXwh4+XEWYM4BdXT+KZhV+jHZ+U0wb2pshURYCvj8vv/GRTJj8czXdrOc+XUnDr2KG8smwtFWYLv7l2KrvyCjleUe1MMzYtCUtdPc99sozhKT24IWMQ81dtpqauntdWrKfSUktseAiPzriC3360BICVuw9ysKgEL4Pi8ZkT6d+jO/ua3eB4IqUUd44fwV++WoXJbOWZG6ezM7eAIlOzttIvBXNdPb/9cAkZqQncNGowc1duIDY8hJGpCfx+4VLCjAH8cvZkfvfREhqbbPx58SrqGhvxMih+c9009uQVkV1cBkBiVDiBfr6dVeRzopT9huLvX6/BZLbw9PXT2JVbSFGztjIuLRlLfT2//3gpI1J6csPIdN78bhMNTU0s3raHuIhQ4sNDnenrGhp5adEK589PXz+NHTkFbi3XhVIKbhiZzrxvN1JpsfLYzAnszT9OcWWNM83IXglY6xv43y++ZXBiHLOG9mfBOnuQqKzGzF+/Xt3qvO+t3UZdQyNgD46kJ8SRmVvonkIJt+mS013iIkIxmS1UWKzYtGZv/nHSYqNc0qTFRpGZZ2+w+wqKSY6KACAyOIicYnsk1FLXQG1DA3HhISgABb5eXgD4eXtTba1zW5ncrTZzD01V1WdPeAkYltyD9Vn2m60jJ8oI9PUlNNDfJU1ooD8Bvj4cPlEKwPqsbIal2AejhaYql0HJpSD+5DVkbnYNxUW7pEmLi2JX7slr6ATJ0fZrKCrEyFHnNVRPneMa6oqSoyMoqaqmtNpMk83G1sN5DEmKd0kzJCnO+aRue3Y+feNiHK/Hs/VwHo02G6XVZkqqqp11FG4MYFBiHOsOdO2b/KSoCEqqaiirNtNk02zPPsbghDiXNOkJcWw6nAvAjqMFznaUX1ZBpaUWgCJTFd5eBrwNBhqamjhYVAJAk01zrMxEuDHAjaU6P8nRERQ3aytbjrTVVuLZcPAoANuzj9E3/lRb2XLkVFspbtZW6hrtAy0vgwEvg0Jjv8lTSnHz6CF8ummnu4p4QZKiIiiuqnHUj2brkWOkJ7rWT3pSHBsd19IPR/PpG2+kSQkYAAAgAElEQVRvK/WNTRw5UUZjY9Npzx8dEkRwgB+Hj5d2WBk6QkK3cEqrzZTXWGiyaXbmFDCgR3eXNAN6dGd79jEAducV0bt75FnPe3KAblAKL0OXHMpdOIPBff86q4jtSaSUilFKzVdKLXX83F8pdX/HZu30Qvz9qGo2gK6y1hEc4OeSJtjfnyqr/QNCa01tQyMBvj6cqKymT2w0SinCAv2JDQshJMAfm9Ys3bGfh6aN4YlZE4gMMbKzi92xi7ZFGAMpd0xPACg3W4gwBrZKY2qepqZ1mrZEhQTx4i0z+e310+jT4kbRkwUH+DsHUABV1to2r6HK01xDaXFRjmsowHkNnXTtiAE8OHU0V/RNcU9hLkBYYADlNVbnzyazhbAWA8awZm3DpjXW+gaC/H0JMwZQbrY0O9ZKWKD92FvHDuXTTZnY2nj+fMPIQfzhphncMmYI3h7+4RoWGIDJ3Kx+LFZCW9VPACZHHZ6sH2OL6O/QpHjyyypotNlcXg/w9WFQz1gOFBZ3UAkunnBjgEsfYaqxtrq5aJ6meVs507FKKX5/0wz+8pPr2Zd/wnkDPGVgbzJzC1yuU0/WsowVZkur+mnenk7XVk5nRGpP50C2KwkJ9KfCcuoaqrTUEhroWi+hzdLYHH3tyScoEUGBPDFrIg9PH+sMNp70wJTR/OGmGdQ1NLIrT6Lol6L2Tnd5G3gL+J3j54PAQmB+B+Tp7NqYhtTyo/B03xa7M7eQyGAjD0weRaXFyrHySmxaY1CK4Sk9eOO7TZjMVq4anMa4tGTWZR296NkXna/V0KmtNnWWCX4VZiu/eOdzaurqSYqK4PGZE3j6w6+odUQ4upxWF1HbyXbkFBIZHMScKaOotNRyrLzCORj9fMtuqmvr8PX24ubRg0lPiGVXXlHH5vsCtOdbpdtKovXpj01PiKXKWkdeqanVjdsix9oYb4OBH08YwVVD+vLVD/vOPeOd6RznvcaGhXB9xiDnvPyTDEpx36RRrNp32Dlv27O1/oO37iJOl+b0x2qteeHTbwjw9eFnM8YTFx6Kua6OESk9eXnxdxeca/dpo4wtGsuFfIt7RmoCb32/+fxP0Ena7j/acRFpTZW1jpcWrcBS30B8RCj3TBzJn79a5Yyiv/ndJrwNBu4YP4xeMVEcOl5ycTPv6S6kQXUR7R2kR2qtP1ZKPQ2gtW5USp32uZRS6kHgQYC5c+dCZPKF57SZKmsdIc2ifiEBftS0mJpSZa0lJMCfamsdSin8fbydi7VW7D7oTHfPxAzKayzEhAUDOO/y9+WfYGzaxc23cJ+pA/swaUAqAEdPlBMRdCoqHmEMxNQsAgr2yHl48zRBgZiaRT/a0mizUeNYFJlTUk5xVQ2xYSHOhaWerNpa6zLlJyTAn+rautZpTnMNNV88eO+kkc4nFSfPUd/YxJ5jx4mLCPXoQbrJbCUi6FRUK9wYSIXZ2iKNvW2YzFYMShHg64O5rh5TjdXlaUu4MYAKi5XBiXEMSYxjUEIsPl4G/H18uH/KKOZ/t9kZFW202VifdZQZg/u6p6DnqcLiGi0ODwygssV1UWG2Eh5kL3vz+gF75PTBaWN4Z/VWl/UOAHeMH0ZxVXWrRXSe6mQ7OOlkmdtK06qttONYa30DWYXFDEzoTpGpiujQIP54x2wAfL29+ePtV/PbD5d0YAkvTMsyhhkDqTDXtkhjb08V5tZt5UziI0IxGBR5pZ69yLotlZZa5xM2sEfNTz7lb5nm5MJ8fx9vLI6+1lJvf/pUUF5JWY2ZqGAj+eWVzmMbbTb25p9gQM/ul98g/TLQ3metZqVUNxwxFKXUaKDydIm11vO01iO01iMefPDBi5BNV4WmKiKCAgkL9MegFAN6dHfOcTzpYFGJc+5k//hochwDJ28vAz5e9mInR0dg05rSajPV1joiQ4IIdCzySonp1upDRXQd3+45yLMLl/LswqVsP3qMcWn2qRepMd2w1Ne3eoRcaamltr6R1JhuAIxLSznr4r5gfz+U404+KiSImNBgiqtqzniMpyhwXkMBp66hFlMOsgpLSE88eQ3FOG8+7NeQfe1GSnQENpv9GlKOD12wR0l7d4+ipNKz6yOnuJzo0GAig414GQxk9EogM9d1mtvO3ELG9kkCYHhKD7IKTwCQmVtARq8EvA0GIoONRIcG23dZ2LKbXy/4kqc/+Ip5KzeSVVjM/O/sEcDmN0ZDk3tQUH7abtQj5JaYiA4JoltQIF4GxfCUnq1uunblFTG6VyIAQ5PjyXK0owBfHx65chxfbNvjXAh50jXDBxDg48OnXWiXjpzicmKatZWRqQlktpgSmZlTwNg+9uDO8JSeHDjZVnIKGJl6qq3EONpKkL+f85rx8fKiX4/uHDdVszuviF+9+wVPLfiSpxZ8SX1jo0cP0KFZWwm2t5WM1J6tpmDsyi1kjONaGpbcw9lWziYjNYGth/Mudpbd4lhZBZHBRsKN9noZkhTPvvwTLmn25R9neEpPAAYlxDrXRhn9fJ3B4oigQCKDjZTVWPD19nJOTzQoRd+4aIorL611U+2hlHLbv87S3kj6L4HFQKpSaj0QBdzUYbk6C601y3Zmcce4YSilyMwtpKTazMR+qRRVVHGwqIQdOYVcP2IgP7tyHNb6BhZt2Q3YG/2d44ahtaaqto4vttq3u6qprWPN/mx+MmEETVpTaall8fa9nVXEDtf9uacIGJKOV1goSYvep3z+e1Qtab3jyaUgM7eQwYnxvHzXtdQ3NvHmtxud771460zn7izvrN7CnKlj8PH2YlduoXPR5PDkHvx4QgbBAX78cvYk8kpNvPzlKtLiorlxVDo2m8amNW+v3tKuqJAn0FqzdOcB7hxvv4Z25hRQUm1mUv9UCk0nr6ECbsgYyM9njHduYwqOa2j8cLTW9q1Kt9mvLW+DgTvHD8fLYO/UjhaXefwuJjat+WDdDzwxayLKsW1coamKa0cMJLeknMzcQtYdyOb+yaN56bZZmOvqmbfS3n4KTVVsO5LH87fMxKZtfLBu+1kfYz8wZTRBjpu7Y2Um3l+z3R3FPG82rVm4cSc/v+oKDEqx8WAORRVVzB7Wn9xSE7vzithw8Cj3TBzJczdfhaWunvmr7DckE/unEhUSxMwh/Zg5pB8A/1i2Fm+DgZlD+nG8ooqnrp8GwOp9h1tto+tp7G1lO09cPRGDMjjbynUjBpLjaCtrD2TzwJTR/PH2qzHX1TN3xQbA0Vayj/HCrbOwaRsL1trbSligP/dNGY1BKZSCrUeOddm5xTatWbhhB4/NnIBB2bfrLDJVcc3wAeSWlLMrr4j1WUe5d9JIXrhlJpa6epddoV66bRb+Pj54eRkYnBjHK0vXOHeGGZ7Sg38uW9dZRbsgNq35z9bdzJlq/ztvOZLHicpqrkxPI7+8gn35J9hyOI/bxg3jN9dNxVJX79zZJSW6G1cOTsOm7Z8xn23e5Vjn4Me9k0bibfBCKTh8vJRNh3I7uaSiI6h2zY0ClFLeQBr2KVZZWuv2biCuX2y2hZKAZ2+czqHxMzo7Gx6l9zr7DcLd/1rQyTnxLO/+7E5e+Gx5Z2fDo/z+R1cCMGfuwk7OiWd546FbeWT+p52dDY/y6v32WNIDr3/UyTnxLG8+fBsPv/FJZ2fDo7w+52aefH9xZ2fDo7x817Vw2tVJna/ody+6bWf42JeePWs9KKWuAv4OeAFvaq3/p8X7CcA7QJgjzVNa66/PdM727u7yMyBIa71Xa70HCFJKPdKeY4UQQgghhLhUKaW8gH8BM4H+wO1Kqf4tkj0DfKy1HgrcBrx6tvO2d076HK21c8WG1toEzGnnsUIIIYQQQlw8Srnv39mNBA5rrbO11vXAR8B1LdJo4OSXioQCZ53b1t456QallNKOuTGOO4au8TVoQgghhBBCdJx4oPlG/vlAy68pfw5YrpR6FDAC08520vZG0r8BPlZKTVVKTQE+BJa181ghhBBCCCEuHjdG0pVSDyqltjX713Lrwja3xG/x8+3A21rrHsAs4D2l1BnH4e2NpP8GeAj4qSMjy4E323msEEIIIYQQXZLWeh4w7wxJ8oGezX7uQevpLPcDVznOt1Ep5Q9EAqfdi7Rdg3SttQ14zfFPCCGEEEKITqMM7Z0M4hZbgd5KqWSgAPvC0DtapMkDpgJvK6X6Af7AGb+Bql2DdKXUOOxzaRIdxyhAa61TzqEAQgghhBBCXFK01o1KqZ9jnx7uBfxba71XKfUCsE1rvRj4FfCGUuoX2KfC3KPPsg96e6e7zAd+AWwHms63EEIIIYQQQlxqHHuef93itd83+/99wLhzOWd7B+mVWuul53JiIYQQQgghOkT7tkbs0to7SF+llHoZWATUnXxRa/1Dh+RKCCGEEEKIy1h7B+kn93oc0ew1DUy5uNkRQgghhBDiLAwSSQdAaz25ozMihBBCCCGEsGtvJB2l1NXAAOxbxgCgtX6hIzIlhBBCCCHEaV0Gc9LbtcmkUup14FbgUezbL96MfTtGIYQQQgghxEXW3kj6WK11ulJql9b6eaXU/2FfRCqEEEIIIYRbediXGXWI9pbQ6vivRSkVBzQAyR2TJSGEEEIIIS5v7Y2kf6WUCgNeBn7AvrPLmx2WKyGEEEIIIU5HXfqR9Pbu7vKi438/U0p9BfhrrSs7LltCCCGEEEJcvs44SFdK3XiG99Bay7x0IYQQQgjhXrJPOtec4T2NLB4VQgghhBDiojvjIF1rfa+7MiKEEEIIIUR7qMtgn3T5MiMhhBBCCCE8TLsG6Y4vMwoEJmPf1eUmYEsH5ksIIYQQQoi2XQa7u7S3hGO11ncDJq3188AYoGfHZUsIIYQQQojLl3yZkRBCCCGEEB5Gaa3PnkipZ4F/AFOAfzleflNr/Ww7fsfZf4EQQgghhPA0Hrs6s/jP/3Db+DL6vx7tlHpo78LRPwM/Ba4ANgJrgdc6KlNCCCGEEEJczto7SH8HqAZecfx8O/AucEt7Dp4zd+G55+wS9sZDt3L3vxZ0djY8yrs/uxOAQ+NndHJOPEvvdd/w5PuLOzsbHuXlu64FYO7KjZ2cE8/y0LQxzHjp9c7Ohkf55ncPAzD9/0lMqbkVz/yU2/72bmdnw6N89MTdXP0/8zo7Gx5lyVMPdnYWzky2YHRK01oPbvbzKqVUZkdkSAghhBBCiMtdewfpO5RSo7XWmwCUUqOA9R2XLSGEEEIIIdqmDJd5JF0ptRv7wk8f4G6lVJ7j50RgX8dnTwghhBBCiMvP2SLps92SCyGEEEIIIdrrMvgyozMO0rXWue7KiBBCCCGEEMKuvXPShRBCCCGE8AyXwe4ul/6zAiGEEEIIIboYiaQLIYQQQoiu5TLY3UUi6UIIIYQQQngYiaQLIYQQQoguRRku/TjzpV9CIYQQQgghuhiJpAshhBBCiK7lMtgn/dIvoRBCCCGEEF2MDNKFEEIIIYTwMDLdRQghhBBCdC2yBaMQQgghhBDC3SSSLoQQQgghuhSlJJIuhBBCCCGEcDOJpAshhBBCiK5FIulCCCGEEEIId5NIuhBCCCGE6FoMl36c+dIvoRBCCCGEEF2MRNKFEEIIIUTXchnMSe9Sg/QBPbtz29ihGJRi7YFslu084PK+t8HAfVNGkRgZTk1tPfNWbqCsxgLAzCH9GN83GZvWfLR+B3vzjzuPU0rxzI3TqTBb+ceytQD0jY/mplFDMCiobWjkre+3UFJV477CXoC7rhjO4MR46hoaeePbjeSWmlqlSYqKYM7UMfh6e5GZW8D7a7cDkJGawA0jBxEXHsrznyzjaEk5AJHBRv7njtkUVVQBcOR4GW+v3uK+QrlJ9NO/xDh2FE2mCvLufqizs9Oh0mKjuDZjEAal2HI4l1V7D7u872UwcNvYofToFoalrp73127DZLYSbgzgyWumOK+H3FITi7bsAuDh6WMJDvCnsbEJgHnfbsRcV+/egl0kR/fu4vtPP8BmszFo3ARGXjnb5f38Q1l8/9kHlBQc4+p7f0qfYRnO96rKy1ix4N9Um8pBKW545BeEdotydxEuqp9eOY6RqQnUNjTyf1+t4vDx0lZpenWP5L+umYyftzdbjuTx2vL1AKTEdOOxmRPw9faiyWbjn8vWkVVYTHpCHM/dPIPjldUArD9wlAXrtru1XBfikSvHMbJXInUNjbz85Xdt1knv7pE8ee0UfL292XI4l1cddZIa043HZ0501skry9aSVVhMkL8vv5o9mbjwUOobG/m/r74nx9EPdxU/mZjB0OR46hqaeG35+jbznxwdwU+vHIevtxc7jhbwzuqtANwyZgjDU3qi0VRZanlt+XpMZivDU3pyy5ghaDRNNhvvrt5GVmGxu4t23h6aNpYRqT2pa2jkr0u+58iJslZpesVE8ourJ+Hr48W2I8eYu3IDAL+5bio9IkIBMPr7Ya6t49G3FtEnNopHr7rCfrBSfLBuOxsP5rirSMINuswgXSnFHeOG89cl32MyW/ndjdPJzCl0DhoBxvdNwVJXz+8++pqM1J78aPRg5q3cSGxYCBm9EvjDx8sIMwbwi6sn8czCr9FaAzBtYG+KTFUE+Po4z3XX+BH885u1HK+oZlL/Xswe1p+3vvf8QWl6YhwxoSE8+f5iUmO6cc+kkTz/6Tet0v1kYgZvrdrM4ROl/Gr2ZNIT4tiVV0hBeQWvLF3DvZNGtTqmuLKGZxcudUcxOk3V18up/GwxMc882dlZ6VBKwQ0j05n37UYqLVYemzmBvfnHKa48dSM6slcC1voG/veLbxmcGMesof2dA6iyGjN//Xp1m+f+cN128ssr3VKOjmKz2fju4/f40aNPEhwWwYI/PU/qoKF0i413pgmOiGDGjx9g28rW18Syd+cxasY1JPYbSH1tLaqLfzNeRmoC8RGh3Pvah/SNi+bRq67g8bc/b5XusZkT+PvXa9hfcIL/d9ssRqT2ZNuRYzwwZTTvr93GtiPHyEhN4P4po/n1+4sB2HPsOL//uOv1KyNTE4iPCOOeVz+gX3wMj82cwGNvLWqV7rGZE/jrktXsLzjBS7ddTUZqAluP5DFn6hjeW7uNrUfyGJmawJypo/mv9xZz+7jhHDlRxvOffkPPbmE8etUV/HrBl51QwvMzJCme2PAQnnj7P/TqHskDU0fxzEet/773TxnNG99u5FBRKU9dP5UhSXHszCnky+17+XjjTgCuGtKXG0elM/+7zew5VsT27GMAJESG8fisifzq3S/cWrbzNSKlJ3HhIcyZu5C0uGh+NuMKfvnuf1qle2TGeP6xbA0HCot5/uarGJ7Sk+3Zx/jfL751prl/ymgsjsBHbkk5j7/9OTatCTcG8M/7bmLzoVxsjrHNpU72SfcgydERlFRVU1ptpslmY+vhPIYkxbukGZIUxwbHXeT27Hz6xsU4Xo9n6+E8Gm02SqvNlFRVkxwdAUC4MYBBiXGsO5Dtci6Ndg7aA3x9qLBYO7iEF8ew5B6sz7KX5ciJMgJ9fQkN9HdJExroT4CvD4dP2KM+67OyGZbSA4BCUxXHK6rdm2kPUpu5h6aqS7/8Cd3CKa02U15jocmm2ZlTwIAe3V3SDOjR3fmhuDuviN7dIzsjq53ieE42YVExhEVG4+XtTd/hoziya4dLmtBuUUTF92z1QVFWVICtyUZiv4EA+Pr74+Pr57a8d4QxfZJYuesgAAcKizH6+xERFOiSJiIokEBfH/YXnABg5a6DjO2TDIDWYPT1BcDo50t5tdmNue8YY9KSWLk7C4D9BScIOl2d+PmeqpPdWYxNSwJAa02gn/0zxujvS1m1/alvYmQ4O3LyAThWVkFMWDBhxgB3FOmiGJHakzX7jwBw+Hgpgb6+hAW65j8sMIAAXx8OFdk/g9bsP8KI1AQArPUNznR+PqfiiHUNjS1e7zoD0dG9k/huzyEAsgqLMfr5Et7ibxpuDCDQz5cDjqcD3+05xJjeSa3OdUXfFFbvsz/1rGtscg7Ifb290V2oTkT7tDuSrpQaCyQ1P0Zr/W4H5KlNYYEBlNecGiibzBaSo7u5pjEGYnJMb7FpjbW+gSB/X8KMAWQXlzU71ursNG4dO5RPN2Xi7+NaFe+u3spjMyfQ0NiEtaGB//58ZUcV7aKKMAZS7qgDgHKzhQhjIJWWWpc0puZpauxpziYqJIgXb5mJtb6BTzdncrCo5OJmXrhNSKC/y41npaWWhMhwlzShzdLYtKa2oZFAP/tAKyIokCdmTaS2oYFvdh5wTosCuGXMULTW7D5WxMrdB91QmouvpsJEcHiE8+egsHCKcrLPcMQppuLj+AUGsnjeP6gsKyEhrT9XXH8Lhi68E0FksNFlul9pVQ3dgo0ufU23YCOlzQbfpdU1RAYbAXh9xXr+ePvVzJk2BqUUv2gWhe8XH8NrD9xEWY2FN1a2PT3PE0UGGyluUSeRLeokskWdlFSZnXXy2vL1/Pcds3lw2lgMwOPv2Osku7iM8Wkp7D12nLS4aGJCg4kKNlJh7hqBoghjoPOGAxyfL0GBLv1NRFCLz6lq18+gW8cOYUK/VCx19bzw2XLn6xmpPblt3DBCA/1dosuerltwICXVzdpKtZluwUZMzf6m3YKNlLVK4/q5PKBndyrMVgpNp2YQpMVG8fisiUSHBvN/X626bKLowGWxu0u7BulKqfeAVGAn0OR4WQNuG6S356lGW0m0Pv2x6QmxVFnryCs10SfWdb7otPQ0Xlm6hqPF5Vw5OI1bxgzl3TVbzz3jHqDVJdtGfeizXNgVZiu/eOdzaurqSYqK4PGZE3j6w6+obRbdEF1H29dKOzp3ramy1vHSohVY6huIjwjlnokj+fNXq6hraOSDdT9QZa3Fz9uLuydkMDy5B9uP5l/0/He81nXR3iertiYbBYcPctfTzxMS3o2v/v0qezetZdDYiRc5j27Ujj6jzTblqMfZwwcwd8UG1mUdZUK/VH45exJPffAVh4+X8ON/vk9tQyMZqQn84earuO+1DzugABefaqPE7bmGTiaZPXwAr63YwLoD2Uzol8qvZk/mNwu+5KP1P/DIjPG8/sDNHC0p4/DxUppsXWjg1VZbaXk9nSXNwg07WbhhJ9dlDGTG4L58uikTgK1HjrH1yDH6xkdzy5ihvLRoxcXMeYdpq620rpI22lOLnyf268Xq/a5rh7KKSnhk/qf07BbGL66exLYjx2hoakJcGtobSR8B9Nft+hQHpdSDwIMAc+fOBULPL3fNmMxWIoJOPR4KNwa2iiyYzBbCgwIxma0YlCLA1wdzXT2mGqvLXXq4MYAKi5XBiXEMSYxjUEIsPl4G/H18uH/KKBZu2EmPiDCOFtujg9uOHOPxWRMuuAwdZerAPkwakArA0RPlLo9cI4yBmMwWl/TlNfZ6cqYJCsR0luk8jTYbNY55cDkl5RRX1RAbFuISQRVdR6Wl1uURdGigP1XW2jbTVFpqMSiFv483FsejaEu9DYCC8krKasxEBRvJL690nqOusYkdOQX0jAzvkoP0oLAI+6JPh5oKE0Gh4Wc4otmx4eFE90wgLDIagF7pwyjKOdIh+exI1wwfwMyh/QA4WFhCVEiQ873IkCCXSCjYI38no8QAkcFBzojq9EF9nItI1+w/whNX229YLM2mNmw9ksfPDVcQEtC6LXqKa4cPYNbQ/gBkFRUTHRLEXsd7kSFBzo0KTmpZJ1EhRspq7JH1K9PTnItI1+w/wi9nTwLsdfLnL1c5j3nv53dyvNnaK090ZXoaUwb1BuybCjSPAEcEBWKqcf18Ka+2uH5OBQe6RJVPWn/gKL+5bopzkH7SgYJiYkKDCPb3o7q27mIW5aK5elh/rhrcF4CDRSVEBQcB9mlPkcGn2sFJpdU1dAtudo0FGylv9kTCoBRj05LaXAsC9qlRdQ2NJEaFt7mA+ZIkc9Kd9gDdz5rKQWs9T2s9Qms94sEHHzy/nLWQU1xOdGgwkcFGvAwGMnolkJlb4JJmZ24hY/skATA8pQdZhfYLIjO3gIxeCXgbDEQGG4kODeZocTmfb9nNrxd8ydMffMW8lRvJKixm/nebsdTVE+DrQ0yo/YLpHx9DkclzO8lv9xzk2YVLeXbhUrYfPca4tBTAvnuApb7eZaoL2AdftfWNpMbYpwuNS0vhh7MMpIL9/Zxzb6NCgogJDXZ51Cu6lmNlFUQGGwk3BuJlUAxJimdf/gmXNPvyjzM8pScAgxJinWsYjH6+zr4xIijQ8YFjwaCUczqMQSn6xcd4/ODidLonJlNRfILK0hKaGhs5sH0zKYOGtvPYFGotFizV9rIfO7ifbt3jOjK7HeLL7Xt55M1PeeTNT9lw8CjT0vsA0DcuGktdfatBenmNBUt9A33j7Dcn09L7OHeaKKuxkJ5gr4MhSfEUOhYWN5+XmxYXjUHhsQN0gMXb9/Lwm5/w8JufsD7rKNMGpQH2KTvm2ro268Ra30C/ePv6qGmD0tiYlQM46iTRXidDk+IpcNSJ0c8Xb8dj/JlD+7E7r8jlZsYTLd+VxVMLvuKpBV+x7UgeE/rZg0a9ukdiqW9otaarwmKltr6BXo51LhP6pbLtiH39S/ewYGe64Sk9nVM7YkJPvZ4UFYG3l5fHDtABlvywj0ffWsSjby1i06Ecpgy038SkxUXbg4etgoxWrPX1pDmunykDe7PpUI7z/aFJ8eSXVVDWbPpUTGgwhmafy/ERoRRXXvprqi4n7Y2kRwL7lFJbAOdVobW+tkNy1Qab1nyw7geemDURpRTrs7IpNFVx7YiB5JaUk5lbyLoD2dw/eTQv3TYLc10981ZuBOyLIbcdyeP5W2Zi0zY+WLf9jI8lbVrz3pqtPDx9HBqw1NXzdhfY2QUgM7eQwYnxvHzXtdQ3NvHmtxud771460zn7izvrN7CnKlj8PH2YlduIbtyCwEYntyDH0/IIDjAj1/OnkReqYmXv1xFWlw0N45Kx2bT2LTm7dVbuuzWemfS/bmnCEB3P8MAACAASURBVBiSjldYKEmL3qd8/ntULWm9O05XZ9Oa/2zdzZypo+1bMB7J40RlNVemp/H/2bvv+Kiq/P/jrzMppDdCEhJaQu+9V1FURFl17WXtjfW7RXdX/a3rqtvXLbruWhB1XQUVEQtFAUGQ3nuHJAQSIJ30Ovf3x4QxIQFGZCYz8H4+HvMIM3PuzOdc7p175nPKHMkvZNeR46w7kMEtIwfwxA8upayyyrmyS0pcSy7v2xW75TgWPl67jfKqagL8/Hhg/DD8bDaMgf3Hcll74FAz1/Tc2Pz8uOSmO/j4P3/DstvpNXw0sYlJrJw7m4R2yXTs059jh1L5fOrLVJSVkrpjC6vnfcJdv/kjNpuNsdfdzKx//RULiG/bnt4jxzV3lb6XdQcyGNyxHW9PuZXKaseygCe9cv8NTJk2C4CXv1zOL66+xLmE3PqDGQC8OG8Zj1w+Ej+boaqmlhfrVgYa3T2Fqwf0pNZup7Km1mfm/oBjnwzt1J53fnwbldU1DbLfr91/Iw9P+wiAf33xDb+4ZjwtAvxYfyCDdXX75B/zljLl8lHf7pN5SwFoFxvNEz8YT63dIiO3gL/P/brRe3uzzemZ9EtO4qW7r6OypobXFq5yPvfn26/myelzAXhzyVoeuXwEgf7+bEnPZEu6I+l268gBJEZHYLcc2eVpi9cAMLRzO0Z370it3U5VTS0vzf/G85U7R+sPHmZQSjumPXSLYwnG+Uudz718z/X8X92qQP9ZsIKfTxpHC39/NqQeZkPdxH2AMT06smxXwx65Hm0SuHFYX2rtduwWvLJwBUXl3vvFRb4748oIFmNMk4MpLctqeg22U4o98PqH3zWuC9obD93Mj/4zvbnD8Cr/+/HtAOwfdUUzR+JdOq9YwC/rlqoThxfucOQGXv9q9VlKXlweumw4V/zhteYOw6ss+PXDAEz4/avNHIl3WfT0I9zyosemlPmED372Iyb9eWpzh+FV5j35IDQ93cQr5L/7occma8TceXOz7AeXMukuNsZFREREROQ8cHV1l2IaTzQ+AWwAHrcsy7W1yUREREREvidf/5E4V7g6Jv0fQBYwA0fXxy04JpLuBd4CxrkjOBERERGRi5GrjfQrLcuq/zvxU40xayzLet4Y8//cEZiIiIiISJO0BKOT3RhzkzHGVne7qd5zPvQrCyIiIiIi3s/VTPrtwEvAKzga5WuAO4wxwcCjbopNRERERKQx42qe2Xe5urpLKnDNaZ5ecf7CERERERGRMzbSjTG/sizrr8aYl2liWItlWT9xW2QiIiIiIk3Q6i6wu+7vBncHIiIiIiIiDmdspFuWNafu7zueCUdERERE5CwugtVdXP0xo1bAE0APIOjk45ZljXdTXCIiIiIiFy1Xp8ZOxzH0JRl4DkgH1rspJhERERGR0zM2z92aiavv3NKyrDeBasuyllmWdS8wzI1xiYiIiIhctFxdJ7267u9RY8wkIAto456QRERERETOQKu7OP3eGBMJPA68DEQAP3dbVCIiIiIiFzFXf8xobt0/TwCXuC8cERERERFxaUy6MSbFGDPHGJNrjMk2xnxmjElxd3AiIiIiIqcyxnjs1lxcnTg6A5gJJACJwEfA++4KSkRERETkYuZqI91YlvWuZVk1dbf3AMudgYmIiIiINMlmPHdrJq5OHP3aGPMk8AGOxvnNwDxjTAyAZVn5bopPREREROSi42oj/ea6vw+d8vi9OBrtGp8uIiIiIp5ha74fGfIUV1d3SXZ3ICIiIiIi4uBSI90Y4wdMAjrU38ayrH+4JywRERERkdMwyqSfNAeoALYDdveFIyIiIiIirjbS21iW1cetkYiIiIiIuKA51y/3FFf7Cr4wxlzu1khERERERARwrH9+9kLGXAe8h6NRXw0YwLIsK8KF99B66iIiIiK+x2vT1UXzF3qsfRlx1eXNsh9cHe7yd2A4sN1ypVV/iuc/XvhdN7mgPfPDy7VPTvHMDx0dNb987/NmjsS7vHDHZPaPuqK5w/AqnVcsAOD3sxc1cyTe5enrJ3DXKzOaOwyv8s6U2wAY9+y/mzkS77L02Ue5+cV3mjsMr/Lhz+7ikWmzmjsMr/Lq/Tc0dwgXPVcb6fuBHefSQBcREREROa8ugjHprjbSjwJLjTFfAJUnH9QSjCIiIiIi55+rjfS0ultg3U1EREREpHlonXQHy7Kec3cgIiIiIiLi4OovjrYCfgX0BIJOPm5Z1ng3xSUiIiIictFydbjLdOBD4GrgYeAuIMddQYmIiIiInI6xXfgTR10d0NPSsqw3gWrLspZZlnUvMMyNcYmIiIiIXLRczaRX1/09aoyZBGQBbdwTkoiIiIjIGWgJRqffG2MigceBl4EI4Gdui0pERERE5CLm6nCXGwFjWdYOy7IuASYA17kvLBERERGR07DZPHdzgTHmSmPMXmPMAWPMk6cpc5MxZpcxZqcx5qw/Ee1qJr2PZVmFJ+9YlpVvjOnv4rYiIiIiIhckY4wf8B8cSewjwHpjzOeWZe2qV6Yz8BQw0rKsAmNM3Nle19VGus0YE21ZVkHdG8V8h21FRERERM4b411j0ocAByzLSgUwxnwA/ADYVa/MA8B/TralLcvKPtuLutrQ/juwyhgzC7CAm4A/uB67iIiIiMgFKQk4XO/+EWDoKWW6ABhjVgJ+wLOWZX15phd19RdH/2eM2QCMBwxwff0UvoiIiIiIx7g4Vvx8MMY8CDxY76GplmVNrV+kic2sU+77A52BcThWSFxujOlVfzj5qVweslLXKFfDXEREREQuGnUN8qlnKHIEaFvvfhscy5WfWmaNZVnVQJoxZi+ORvv6072o576GiIiIiIicD8Z47nZ264HOxphkY0wgcAvw+SllPgUucYRuYnEMf0k904uqkS4iIiIico4sy6oBHgUWALuBmZZl7TTGPG+MmVxXbAGQZ4zZBXwN/NKyrLwzva5WaBERERER32LzqtVdsCxrPjD/lMeeqfdvC3is7uYSZdJFRERERLyMMukiIiIi4lOMufDzzBd+DUVEREREfIwa6SIiIiIiXkbDXURERETEt7i2NKJPUyZdRERERMTL+GwmvWN8S67o2w2bMWxOO8LKfekNnvezGa4d1JvW0RGUV1Uza+1WTpRVYDOGqwf0oHV0BJYFC7bu4VBuAQC3jRxAWFALbDZDRm4BX2ze3eg3Xb2dO/bLj8YMIiyoBTW1tQC8t2ITZZVVnq7aOevauhWTB/fGZgzrDhzi650HGjzvZ7Nxy4j+tGkZRVllFe8t30BBaTnRocH88prx5BSVAHAot4DZ67YB8PCEEYQHB1FT49gnUxevptSH9sl3EffUY4SOGEptQSEZP3qoucNpFinxLbmiT1eMMWxJz2TVKedVu5ZRTOjblfiIMGav286erOzmCdSNbh81kL7tE6mqqeGNxWucnw/1dWgVzf3jhxPo78fWQ1lMX7ERgMEd23Ld4N60jo7kuVkLSM/JB6BnmwRuGtYPPz8btbV2Pli9md2Zxz1ar/Pl/yaOZljn9lRU1/DnTxez/2hOozL3jR/GFX27Eh7cgol//PbHC/u0T+TRK0fRMT6W52ctYNmug54M/by6e+wQ+icnUVldw6sLV5JW939dX3JcDFMuH0Wgvx+b0zL577J1ANw0vB+DUtpiASfKKnh14QoKSssB6NEmnrvGDsHPZqO4vILnZi3wZLW+lx5t4rlpWD+MMazcm8bCbXsbPO9vs3HXuMG0axlNaWUV05asIb+kjNAWgTxw6TDat4phzb50Ply9xbnNwJQ2XNnPca3fcfgYn6zb7ulqNT8vW4LRHXyykW6Aif26896KjRSVVXD/+GHsPZpDbnGps0z/Dm0or67m3wtW0LNNApf16sLH67YxILkNAK9/tZqQFoHcNnIA05asAWDW2q1U1TW6bhzWlx5tEth55JjH63eu3LVfAD5Zt52jhUWertL3ZgxcN6QPUxev5kRZOT+ZOIadR46RfaLEWWZIp3aUV1Xzl88W07d9Ilf17+FsXOSVlPLP+cuafO33V2zkSP4Jj9SjORXNX8iJjz8n/ulfNncozcIAE/t2Y/qKTRSVV3DfJUPZd8p5daK8gjkbdjKsc/vmC9SN+rRLJCEynF9Nn0PH+JbcNXYwz3+8sFG5u8YM4e2l6zh4PJfHJ42jT7vWbMs4ypH8E/zry+XcPXZIg/LFFZX8c/4yCsvKSYqJ5JdXX8LP/vepp6p13gzt3J42MVHc/q/36NEmnp9PGsuUabMalVu9L41P1m1j+k/uaPB49oli/vzpYm4e0d9TIbtFvw5JJESH89P/fkLnhFjuu3QYT38wv1G5+8cPZ+ri1ew/msOT115Kvw5JbEnPZM7Gncysa4he2a8bPxzal2lL1hDSIoD7LhnGHz/9irziUiKCgzxdtXNmDNwyoj//+mI5BaVlPPmDS9mWkcWxwmJnmRFdO1BWWcVvP/qSQSltuG5Ib95cspbq2lrmbNxJYnQkidERzvKhLQK5fkgf/vTpV5RUVHHXmEF0TYxj7wWYHLjY+eRwl6SYSApKyygsLcduWew8coyuiXENynRNbMW2Q1kA7Mo8TnJcDACtIkJJy3Z8sy+rrKKyutp58J9soNuMwc9mw/KxPLq79osva9cymtziUvJLyqi1W2xJz6Rnm4QGZXq2SWBj6mEAtmccpXNCbHOE6rUqtu6gtqj47AUvUIkxkeSXllFY9u151aV1qwZlTpRVkF1U4mOfGK4bkJzEyr1pABw8nkdIYCCRIQ0bSpEhQQQF+nPweC4AK/emOb/8Hy0oatAoOSkjt4DCMkemNDP/BAH+fvjbfO+yNLJrMgu27gFg15HjhAW1ICYspFG5XUeOk19S1ujxY4XFpB7Pw/FbJ75rcMe2fLPb8Svn+4/lEhoYSFRIcIMyUSHBBAcGOHsavtmdyuCObQEor6p2lgsK8HeeT6O6prDuQAZ5dV+Mi8or3FyT86dDqxhyikrILS6l1m6xIfUwfdsnNijTt30ia/YfAmBTWibd6q7bVTW1HDyeR3VdL/ZJseGhZJ8opqTC0Xu7Oyub/h2SPFAbL2Nsnrs1E5cz6caYEUCH+ttYlvU/N8R0VuHBQZwo+/YkLSqvICkmsmGZoCBO1J3IlmVRUV1DcGAAx08U0zWxFTuOHCMyOIjWURFEBAeRVeDIEt8+agCJ0ZEcOJbL7iO+1e3qzv0yeVBPLMtid2Y2y/ekeq5S31NESJCzEQCOxlS72OgGZSLrlbHX7ZOQFoEAxISF8LOrxlJRXc2CLXsadN3eNLw/lmWx/fBRvtq+zwO1keYQHtSCovJK5/3i8koSY3z/C+x3ER0aQl69xmV+aRnRoSENPm+iQ0MoKClvVMZVg1LaciingBq7/fwE7UGtIsKcw+IAcopKaBUR1mSD/EIWHRribEgD5JWUERMW0uAzOCYshPySb8vkF5c2OE5uHtGfMd07Ul5ZxXMfO4a0tI6OwM9m45kbriA4IIAvtuxyfhnwdlEhwc4hOwAFpeUkt4ppXKbk22tQeVU1oS0CTzuEMqeohPiocMe+LS2nX/tE/Px878utnJ1LjXRjzLtAR2ALcPIrnQU0SyO9SacmIE4zVGlzehax4WE8MH4oJ8oqOJxfiL1e9mL6ik342WxcP6Q3yXExpGY3Hk/nU87Dfvlk3XaKKyoJ9PfjxmF9nV3YvqCp6rqUrbIsisor+cPsRZRVVZMUE8ndY4fwt7lfU1ldw4y6oQ8t/P340ZjBDExuw8a0I+c9fml+F8ECAmfX5D5oeB41tZ9czQsnRUdy8/B+vDDn6+8amdfy9az4uTBNHASn9kif7Xz6cNVmPly1mWsH9+LKvt34aM1WbMZGSlxLfvfxQgL9/fjdzVex/2iuTwzBdOm8+I6fMWVV1by/cjP3jx+GZVmkZucRGx56riH6LKMx6U6DgB6Wi586xpgHgQcBXn/9dWjZ4dyiO43i8ooGXa0RwUEUV1Q2LhMcRHF5JcYYggL8nV1p9Sdt3DNuSKNsR63dzt6sHLokxvlUI91d++Xka1TV1LLj8DESYyJ9ppF+oqyiQXdrZEhQo67Sk2VOTqANCvCnrG6flFU5snqZ+SfIKymlVXgoR/JPOF+jsqaWzemZtI2NViP9AlVUXklEcAvn/fDgFhSXV55hiwvDpb06M7ZHJwDSsvNoGRbC/rrnYkJDGmQHAfJLyogO+/ZciwkNobD07Jnk6NBgfjJxNFMXrya7Xjba2107uDdXD+wBwJ7MbFpFhDmfaxUR1mDOwoXs8j5dubR3FwAOHsulZb3GYsuwhr0rAHnFZcSEfVsmJjyUgiaOkxV70njyB5fy0Zqt5JeUUlxRQWVNDZU1NezOPE77VtE+0Ug/uQjBSdGhwZwoa7hPCkvLiQ4LprCsHJsxBAcGnHUhgu0ZR9ledx0e1TW5QbJRLhyu9o/sABLOWqqOZVlTLcsaZFnWoAcffPDcIjuDzIIiYsJCiAoJxmYMPdsksO+UCRN7s3LoUzfuq0dSvHOYgr+fjQA/PwBS4mKw2y1yi0sJ8PMjLMgxxMEYQ+eE2Abddr7AHfvF1H1ggGOsfueEVuSc8J0L6eG8QmLDQ4kODcHPZujXIYldpwxj2nXkGANTHGMie7drzYG6MbWhLQKdWZCYsBBiw0PJKynDZoxzOIzNGLonxXPMBy4Wcm6ynOdV0LfnVRMrd1xoFu/YzzMzv+CZmV+wKe0II7smA44VpMqrqhsMdQHHl92K6ho6xrcEHOO0N6VlnvE9QgIDeGzSOD5as5X9x3LdUxE3+XT9du5/7UPuf+1DVuxJ5Yq+3QDHSh6llVUXzVCXhdv28sT0OTwxfQ7rD2YwpnsKAJ0TYimrqm4w1AWgsKyciqpq59yfMd1TWH/QMScoISrcWW5QSlsyCxwT8zccPEy3xHhsxhDo70fnhFgyfWTS/qGcAuIiwmgZ5rgGDUppy7ZDDZNc2w4ddU46H5Cc5NIE0PAgR+IgJDCAMT06snJP2vkP3tsZ47lbMzljJt0YMwdHz0w4sMsYsw5wppAsy5rs3vCaZlkWX2zZw+2jBjiXRMspLmVcj45kFRSx72gOm9MzuW5wLx69YhTlVdV8XLd0XmiLQG4fNRDLsiiuqOTTDY5liwL9/bh5eH/8/WwYY0jPzmdDqm9lRt2xX/xtNm4fNRA/m8EYQ1p2Hpt8KGNstyw+Xb+dBy4d5liC8WAGx08Uc3mfrhzJL2TXkeOsO5DBLSMH8MQPLqWsssq5sktKXEsu79sVu2Vhtyw+XruN8qpqAvz8eGD8MPxsNoxxTJBae+BQM9fUfRKefZLgfn3wi4qkw+z3yH/zXYrm+c7yZ9+XZVl8uWUvt44cgM0YthzKIre4lLHdO5JVWMT+ozm0jo7gxmF9CQoIoHNCLGN7dOT1r1Y3d+jnzdZDWfRpl8gLt19DZU1tg5Wfnr9pIs/M/AKAd5at54Hxwwj092NbxlG2ZTgmqQ9MbsMdowcRHtyCxyaNJSO3kL/N/ZrLenchPjKcyYN6MXlQLwBemLPE53oq1uw/xNDO7Zn+kzuprK7hL58tdj437eGbuf+1DwF4aMIILuvdhRYBAXz02N3M27SL/y5dR9fEOH5/y1WEBbVgeJdk7h43hHteeb+5qnPONqdn0j+5DS/dfT1VNY4lGE/6y+3X8MT0OQBMW7KGKZePJMDfny3pmWxJd3yZu23kQBKjI7BbjiTRG4sdx1lmwQm2HsrkhTsmY1kWS3bu53BeoecreA7slsUHq7bwfxNHYzOGVfvSOVpYxNUDepCRW8C2jKOs3JfG3WOH8NyNV1JWWcWbX691bv/7mycSFBCAn5+Nvh0S+dcXyzlWWMyNw/vSJiYKgPmbd/lUL5S4zpxpBIsxZuyZNrYsq+m16U4p1tRSXRezZ354eZPLl13Mnvnh5QD88r3PmzkS7/LCHZPZP+qK5g7Dq3Re4fiC8PvZi5o5Eu/y9PUTuOuVGc0dhld5Z8ptAIx79t/NHIl3Wfrso9z84jvNHYZX+fBnd/FIE8tmXsxevf8G+M4j5j2nbMNmj43xCRnUv1n2wxkz6Scb4caYv1iW9UT954wxfwFcaaSLiIiIiMh34OqY9AlNPDbxfAYiIiIiIuIKY7N57NZczjYm/RFgCpBijNlW76lwYJU7AxMRERERuVidbQnGGcAXwJ+AJ+s9XmxZlu+sTSgiIiIiFw4f/HXi7+psY9JPACeAW40xfkB83TZhxpgwy7IyPBCjiIiIiMhFxdVfHH0UeBY4Dpz8zWYL6OOesERERERELl6u/uLoz4CulmXluTMYEREREZGzasYfGfIUVwf0HMYx7EVERERERNzM1Ux6KrDUGDOPhr84+g+3RCUiIiIicjq2Cz+T7mojPaPuFlh3ExERERERN3GpkW5Z1nMAxphwx12rxK1RiYiIiIichjEX/hKMLtXQGNPLGLMZ2AHsNMZsNMb0dG9oIiIiIiIXJ1eHu0wFHrMs62sAY8w44A1ghJviEhERERFpmlZ3cQo92UAHsCxrKRDqlohERERERC5yLq/uYoz5DfBu3f07gDT3hCQiIiIicgYXweourmbS7wVaAR8Ds4FY4G43xSQiIiIiclFzNZPeEWiLo1HvD1wKjAf6uCkuEREREZGmXQRj0l1tpE8HfoFjdRe7+8IRERERERFXG+k5lmXNcWskIiIiIiIuuBjWSXe1kf5bY8w0YDFQefJBy7JmuyUqEREREZGLmKuN9HuAbkAA3w53sXBMIhURERER8ZyLYHUXVxvpfS3L6u3WSEREREREBHB9CcY1xpgebo1EREREREQA1zPpo4C7jDFpOMakG8CyLEtLMIqIiIiIZ9k0cfSkK90ahYiIiIiIOBnLstz9Hm5/AxERERE577x2dmZVarrH2peBKR2aZT9c+H0FIiIiIiI+xtXhLt/LPa+874m38RlvT7mVB17/sLnD8CpvPHQzAK9/tbqZI/EuD102nN/PXtTcYXiVp6+fAMD+UVc0cyTepfOKBdz60rvNHYZXef+ndwJw2e9eaeZIvMtXv5nC9X9/u7nD8CqzH79H588pTp4/XusiGJN+4ddQRERERMTHeCSTLiIiIiJy3hivHS5/3iiTLiIiIiLiZZRJFxERERHfoky6iIiIiIh4mjLpIiIiIuJTjE2ZdBERERER8TBl0kVERETEt5gLP8984ddQRERERMTHKJMuIiIiIr5Fq7uIiIiIiIinqZEuIiIiIuJlNNxFRERERHyLlmAUERERERFPUyZdRERERHyK0RKMIiIiIiLiacqki4iIiIhv0Zh0ERERERHxNGXSRURERMSnlAe18Nh7hXvsnRpSJl1ERERExMuokS4iIiIi4mXUSBcRERER8TJqpIuIiIiIeBmfmzh626gB9GmfSFVNLW8uXsOh3IJGZdq3iub+8cMI8Pdj26EsZqzYBEBoi0AeuXwkseGh5BaX8srCFZRVVhPSIoB7LxlKXGQ41TW1vPX1WjLzTwBw7yVD6ds+kaLyCn7z4RceraurerZN4JYR/bEZw/I9qXy5ZU+D5/1tNu4dP5T2sdGUVFQx9atV5JWUATCxX3dGdUvGbll8sHIzO48cc25njOHp6ydQWFrOy18uB+CecUPo0roV5VXVALy9dB2H8wo9VNPvL23nNpbOmoHdbqf3yDEMufzqBs8f2b+XpR/PICfzMJPueYQuAwY7nyvKz2PR9LcoLsgHY7huys+JbNnK01Vwu5T4llzRpyvGGLakZ7JqX3qD59u1jGJC367ER4Qxe9129mRlN0+gzSzuqccIHTGU2oJCMn70UHOH41F3jR1Mvw6Oz+FXF64iPSe/UZnkuBgenjCCQH8/tqRn8c6y9QDcOKwvgzq2xW5ZFJVV8NqiVRSUlnu6CufFj68YxZBO7amsruGvny/mwLHcRmU6J7TiVz8YT6C/P+sOHOI/C1YAjvPsZ1eNJTgwgGOFxfzpk0WUVVXTNTGOn08aB4Ax8L9l61m5N82T1fpe7rtkKAOS21BZU8O/v1xBanZeozIpcS35vytHE+jvx6a0I7z59VoAbh7ej8t6d6GovAKA6Ss2sSntCH3bJ3LH6IH42/yosdfyzrIN7Dh81KP1+r6+zzlz26gBDEhuQ63dzvHCYl5btIqyqmr8bIYHLx1Oh7gY/Gw2lu9O5bMNOzxdNXEjn8qk92nXmvjIcJ6cPpf/Ll3HnWMHNVnuR2MG89+l63hy+lziI8Pp3a41AFcN6MGuI8d4csZcdh05xqT+PQC4ekBPDucW8syHX/DG4tXcNmqA87VW7EnlH3OXur1u58oYw20jB/LS/G94ZuaXDOnUntZREQ3KjOqWQlllFb/+YD5fbd/LD4f1BaB1VASDO7XjtzO/5KX533DbqIEY8+26o5f16szRgqJG7/nRmq08//FCnv94oU810O12O0tmvst1P36Mu3/zR/ZsWEve0cwGZcJjYrjizvvpNmhYo+2//N9UBl02kbuf+RO3/fIZQsIjGpXxdQaY2Lcb76/czGuLVtGzTQKx4aENypwor2DOhp3sOHys6Re5SBTNX0jW479u7jA8rl+HRBKiwvn5O5/xxuI13Dd+aJPl7r1kKNMWr+Hn73xGQlQ4fdsnAjB30y6emD6Xp2bMY1NaJtcP7ePJ8M+bIZ3akRQTyV3/mc4/5y3lp1eNbbLcT68awz/mLuWu/0wnKSaSwR3bAfD41ZcwbfEaHnj9Q1buSeWmEf0BSM/OZ8q0j3j4jZk8NWMuP5s0FpvxjfWgByS3oXV0BD9+62NeW7SKBy8b3mS5hy4bzquLVvLjtz6mdXQE/TskOZ+bu2kXj7/7OY+/+zmb0o4AUFRewR8/+Yqf/+9TXv5iOT+dONoj9Tlfvu85sz3jKL96bw5PTJ/L0cIifjC4FwBDO7fH38+PJ6bP5f+9P49Le3du9Hktvs2lRroxxs/dKxwj9QAAIABJREFUgbiif3IbVu1NByD1eB4hgYFEhgQ1KBMZEkRwYAAHjzu+va/am86A5DaO7TskOTMSK/em0b/u8cSYCHbVZZCPFRYTGx5KRLDjdfcdzaGkssrtdTtXyXEx5BQVk1tcSq3dzvoDGfSr94EHjg+Ik9nQjalH6JYYX/d4EusPZFBjt5NbXEpOUTHJcTEARIcG07t9Iiv2pHq0Pu50LD2VqFbxRMXG4efvT7eBQzm4bXODMpEtW9EqqW2DLysAeUczsdfaad/d8eEYGBREQKDnln/ylMSYSPJLyygsK8duWew8cowurRv2FpwoqyC7qASrmWL0FhVbd1BbVNzcYXjcwJS2LN/t+Fw4cCyXkBYBRIUENygTFRJMcGAA++syy8t3pzKoY1sAZy8cQFCAP5blm0fSiC7JLNq2F4DdmccJCwokJiykQZmYsBBCWgSyO/M4AIu27WVk12QA2rSMYltGFgAb044wulsKAJU1Ndjr9kmgvx++dKIN6diOpbsOAI5rZ2iLQKJDGx4b0aHBBLcIYN/RHACW7jrA0E7tz/i6adn5zt6WjLxCAv398PfznRzj9z1ntmccdR4T+4/lEhNW1xC3oEWAPzZjCPT3o6bW3uD8Et/n6nCXA8aYWcDblmXtcmdAZxIVGkx+SanzfkFpGdGhIZwoq3A+Fh0aQn7dUA6A/NIyouo+JCJDgpxlT5RVOBvih3MLGZjSlv3HckmOi6FleCjRYcHOLjdvFhUSTH7Jt13FBaVlJMe1bFgmNISCun1ityzKq6oJCwokKjS4QVdkQWm584Pj5hH9mbVmK0EBjQ+R64b05pqBPdmdeZzZa7dRY7e7o2rnXUlhAeHRMc77YVHRHE137UtIQfYxWoSE8PnUlzmRl0O7rj0Yfe1N2Gy+c6FwRXhQC4rKK533i8srSYy58HoM5NzFhIWQV+9zOL+kjJiwYArLyuuVCW7wOZxXUtagAXvT8H6M6Z5CWWU1v5u90DOBn2ex4aHkFJU47+cUlRIbHtqg3rHhoeQ2UQYcGfMRXTqwal86Y7p3pFVEmLNct8Q4fjF5PPGR4fz506+cDTRvFxMWQm7xt8dGXnEpMWEhDYYzxYSFkFdc79gobnhsTOzXjbE9OnLweC7/Xbqe0lOSZMM7tyc1O5+aWt+47sD5OWdOGtejE2vqkm5rDxxiYEpbXr3/BgID/Hn3mw2N9pf4NldbGH2AfcA0Y8waY8yDxhiPX7mb6vGzTkkznEuv4LxNuwhpEchzN13JZb27kJFbgN3uGx+KrtS3qSKWdfpt+7RrTVF5JRlNjPefvW4bv/nwC/4wexGhLQK5sl+37xZws2r8f+rq8WKvtZN5YB9jrr+Z23/1W07k5bBzzfLzHF/z85FedWlGTX6eNCrUuFT9dubM1Vt49K3ZrNybxhV9u57P8Dzm1N42aFhHANPE3jpZ5G9zljB5UG9euf8GQloENGh07snK5v7XPuDHb87i1pEDCPDzis7ss2ryGu3SPnEU+nLrHqa8+TGP/+8zCkrKuXvc4Abl2raM4s4xg3ht0arzFrMnnI9zBuDawb2w2+2sqBsR0DE+FrtlMeXNWfz07U+YNKA7cfW+7InvcymTbllWMfAG8IYxZgzwPvDPuuz67yzLOlC/vDHmQeBBgNdff53v81tN43t1ZmyPjgCkZefVdfM4uoOiQ0MoPGXCUf4p3z5j6pU5UVbhzKZHhgQ5M+UV1TW8VTdxBeCFO65pkCHxZgWl5cSEfdtt1tQ+KSgtI7oum2EzhuDAAEorqygoKScmNKTeto5v9n3bJ9KvfSK927UmwM9GUEAA940fyptL1jp7Imrs9roLrO800sOiYhyTPuuUFBYQFhnt2rbR0cS1bUdUbBwAnfoM4Gj6QbfE2ZyKyiuJCP52GE94cAuK62XW5eI0oU8XxvfqDDiGGrYMCwUcwxViwkIoKDnlc/iU7GjLsBAKSss41cq9afxq8nhmrdnmvuDPo8mDenFV3VymfVnZDbLfrSJCG2RLAXKKS4g9tUxdpvlwXiFPzpgDQFJMZJNDPjJyC6ioriE5LsY5PMTbXNmvGxN6dwEcQznqj4luGR7a6P89r6SUluH1jo3wb4+f+r3ii7bv49fXXfZtubAQnpg8nn99sZzjJ7x/mNn5PmfGdE+hf3Ib/jB7kfOxkV2T2Xook1q7RVF5BfuyckiJb0m2j7Rf5OxcHpNujJlsjPkEeAn4O5ACzAHmn1resqyplmUNsixr0IMPPvi9AlyyYz+/nfklv535JZvSMhnRtQPgmBlfXlXd4KQGx0leUV1NSrxjyMeIrh3YXDf5ZEt6pnM84MiuyWxOd0waDA4MwK9u2MKY7h3ZezSHiuqa7xW3p6Rn5xMXGU5seCh+NhuDO7Vj66GGkyG3HMpiRJcOAAxMacPeLMf4yK2HMhncqR3+Nhux4aHERYaTlp3PJ+u286vpc3hqxlymfrWavVnZvLnE8SWm/hyA/sltnKvg+IKE9skUZh/nRG4OtTU17Nm4lpTe/V3cNoWKsjLKih0TaQ/v203LhER3htsssgqKiAkLISokCJsx9GyT4LWNA/GcRdv28dSMeTw1Yx4bDh5mdHfH+OlOCbGUVVY36LYHKCwrp6K6mk4JsQCM7p7CxtTDACREfZu0GZjShqwC3/kM+XzDDh5+YyYPvzGTlXvTmNDH0QvQPSme0oqqBsMVwJE0Kq+qpnuSYx7QhD5dWbXPkQU9ObTQAHeMHsTcjTsBx/45OVE0LjKMNi2jOFbovY3SL7fscU70XHcgg3E9OgHQpXUryiqrGq3cU1BaTkVVtXOuy7genVh3MAOgwfj1oZ3aOXtzQ1oE8uvrJvDeio0+s5rU+Txn+rZP5JqBPfnbnK+pqql1bpNbXErPtgkAtPD3p1NCrE+dT3J2ro5J3w98DbxgWVb9fqZZdZl1j9h2KIs+7Vrzl9uvdizBuOTb7PdzN13Jb2d+CcD/lm3gvvFDCfT3Y3vGUbZlOJZqmrdpF1OuGMmY7h3JKynllQUrAUiMjuCBS4djtyyyCk40yKo/NGEE3RLjCAtqwd9/9AM+Xb/dOQHEG9gtixkrNvGzq8ZijGHl3lSyCoqYPKgXh3Ly2XooixV7UrnvkmH84ZarKK2sYupXqwFHg2zDwQyeu2kidsvOjBUbzzqJ6/7xwwgLaoExhsN5Bbz3zUZPVPO8sPn5cclNd/Dxf/6GZbfTa/hoYhOTWDl3NgntkunYpz/HDqXy+dSXqSgrJXXHFlbP+4S7fvNHbDYbY6+7mVn/+isWEN+2Pb1HjmvuKp13lmXx5Za93DpyADZj2HIoi9ziUsZ270hWYRH7j+bQOjqCG4f1JSgggM4JsYzt0ZHX646pi0nCs08S3K8PflGRdJj9HvlvvkvRvAXNHZbbbU7PpF+HJF6861oqa2p4vd7Qgz/dNomnZswD4K0la3l4wkjHcnKHMtmS7pgkecvI/iRGRWJhkVNUyptL1jRLPb6vtQcOMaRTO/7349uprKnhhc+XOJ977YGbePiNmQC8NH8Zv5w8nhb+/qw7mMG6A44G6SW9OvODQY6J6Cv2pPLlVsfSub3atuaWWwZQU2vHsiz+9cU3PjE/ChwTYAektOGV+35IZXUt/17w7ZDAv985mcff/RyA179aXW8JxkznKi53jhlEcquWdcdGiXNYy1X9upMQHc6Nw/pyY93qZM/PWsgJH9kv3/ecuXvcYAL8/Ph/dT0LB47l8uaStSzctpeHJ4zghTuuAWDZroNk5PrOimtydsaVmfXGmDDLss61/8S655X3z3HTC9PbU27lgdc/bO4wvMobD90McFE29s7kocuG8/t63ZsCT18/AYD9o65o5ki8S+cVC7j1pXebOwyv8v5P7wTgst+90syReJevfjOF6//+dnOH4VVmP36Pzp9T1J0/XjtTqbi42GOTB8PDw5tlP7iaSa8xxvwY6Ak4xztYlnWvW6ISEREREbmIubq6y7tAAnAFsAxoA3jvIDkRERERER/maiO9k2VZvwFKLct6B5gE9HZfWCIiIiIiFy9XG+knf8Kq0BjTC4gEOrglIhERERGRi5yrY9KnGmOigaeBz4Ew4Ddui0pERERE5CJ2xka6Meaxenfvqfv7n7q/oYiIiIiIyHl3tkz6yV+d6AoMxpFFB7gG+MZdQYmIiIiIXMzO2Ei3LOs5AGPMQmCAZVnFdfefBT5ye3QiIiIiIqeo9gto7hDcztWJo+2Aqnr3q9DEURERERERt3B14ui7wDpjzCeABVwHvOO2qERERERETsPy2O+NNh+XGumWZf3BGPMFMLruoXssy9rsvrBERERERC5ermbSsSxrE7DJjbGIiIiIiJyV/SJIpbs6Jl1ERERERDzE5Uy6iIiIiIg3sJRJFxERERGRMzHGXGmM2WuMOWCMefIM5W4wxljGmEFne0010kVEREREzpExxg/4DzAR6AHcaozp0US5cOAnwFpXXleNdBERERHxKZZleezmgiHAAcuyUi3LqgI+AH7QRLnfAX8FKlx5UTXSRURERETOXRJwuN79I3WPORlj+gNtLcua6+qLauKoiIiIiPgUTy7BaIx5EHiw3kNTLcuaWr9IE5s5AzTG2IB/And/l/dVI11ERERE5DTqGuRTz1DkCNC23v02QFa9++FAL2CpMQYgAfjcGDPZsqwNp3tRNdJFRERExKd42QqM64HOxphkIBO4Bbjt5JOWZZ0AYk/eN8YsBX5xpgY6aEy6iIiIiMg5syyrBngUWADsBmZalrXTGPO8MWbyub6uMukiIiIi4lO87ceMLMuaD8w/5bFnTlN2nCuvqUy6iIiIiIiXUSZdRERERHyKHe/KpLuD8UB3wYW/F0VEREQuPE0tLegVDhcUeax92TY6oln2gzLpIiIiIuJTvG1Mujt4pJF+76sfeOJtfMZbj9zClDdnNXcYXuWV+24A4Io/vNbMkXiXBb9+mLtemdHcYXiVd6Y4VrW69aV3mzkS7/L+T+9k/6grmjsMr9J5xQJA16BTvfXILdz/mvZJfdMevoWswpLmDsOrJEaFNXcIFz1l0kVERETEp3jyF0ebi1Z3ERERERHxMsqki4iIiIhPsduVSRcREREREQ9TI11ERERExMtouIuIiIiI+JSLYN6oMukiIiIiIt5GmXQRERER8SkXw48ZKZMuIiIiIuJllEkXEREREZ9iR5l0ERERERHxMGXSRURERMSnaEy6iIiIiIh4nDLpIiIiIuJTlEkXERERERGPUyZdRERERHyK/cJPpCuTLiIiIiLibZRJFxERERGfojHpIiIiIiLiccqki4iIiIhPUSZdREREREQ8To10EREREREvo+EuIiIiIuJT7BfBcBefa6TfNnIAvdu3pqqmljeXrCUjt6BRmfax0dw3figB/n5sP3SUGSs3ARDaIpCHJ4wgNjyU3OJSXl24krKqaud2HVrF8PT1l/HqolVsTD0CQExYCHePG0JMWDBY8M/535BXXOqZyp6DHknx3DisH8ZmWLU3jYXb9jZ43t9m466xg2kbG01pRRVvfr2G/JIyuiXGce3g3vjZbNTa7cxet419R3MI8PPjgUuHERseit2y2J5xlM827Gim2p27Ry4fyZCO7aioruHvc7/mwLHcRmU6JcTyi2suoYW/P+sOZvDqwpUApMS35CcTxxDo70et3c6/v1zB3qxs+rRL5Nkbr+DYiWIAVu5JY/qKjR6t1/d1+6iB9G2fSFVNDW8sXsOhJs6nDq2iuX/8cAL9/dh6KMtZx8Ed23Ld4N60jo7kuVkLSM/JB6BnmwRuGtYPPz8btbV2Pli9md2Zxz1ar/PlrrGD6dchkaqaWl5duMpZx/qS42J4eMIIAv392JKexTvL1gNw47C+DOrYFrtlUVRWwWuLVlFQWu7pKnhU3FOPETpiKLUFhWT86KHmDsct3HENSogK595LhtK+VTSz125jwdZvP7eDAwO4Z9wQkmIisbB4++t1HDye57H6nk3PtgncOnIANmNYvjuVL7bsbvC8v83GfeOH0b5VNCUVVbz+1SrnNXRi/+6M7paC3bJ4f8Umdh45BsDd44bQp30ixeUV/Hbml87XumFYX/q2T6LWbie7qIS3v15Leb1ruC9Yt3oV//7H36i11zJp8rXcdtc9DZ7/fPYsPp01E5vNj+DgYB5/6mk6pKRQXV3NP/70B/bu2YUxNv7vsV/Qb+CgZqqFeIpPDXfp3a418VFhPDVjHu8sW8+PxjR9gN45ZhDvLFvPUzPmER8VRu92rQG4qn93dmce56n357E78zhXDejh3MYYw43D+7Lj8LEGr3X/+GF8uWU3T3/wBb/7eBHF5RXuq+D3ZAzcPKI//164gt99vIBBKW1JiApvUGZE1w6UVVbx7EdfsmTnPq4b3BuAksoqXl20kj98soh3vlnP3WOHOLf5avs+nv94IX/69Cs6xsfSo02CR+v1fQ3u2I6kmEjuefV9Xpq/jP+7cnST5X4ycQwvzf+Ge159n6SYSAZ1bAs4joH3lm9gyrRZ/G/ZBu4bP8y5zY7Dx5gybRZTps3yuQZ6n3aJJESG86vpc3h76TruGju4yXJ3jRnC20vX8avpc0iIDKdP3fl0JP8E//pyOXuzshuUL66o5J/zl/H0h/OZumQ1D1063O11cYd+HRJJiArn5+98xhuL13Df+KFNlrv3kqFMW7yGn7/zGQlR4fRtnwjA3E27eGL6XJ6aMY9NaZlcP7SPJ8NvFkXzF5L1+K+bOwy3cdc1qLSyihkrNrFgy55Gr3XbqAFsP3yUX38wn9/OXEBWQZH7KvgdGWO4fdQgXpy3jN98+AVDOrWjdXREgzKjuqdQWlnF/3t/Hou27eWGoX0BaB0dwZCO7Xjmwy94cd4ybh89CGMMACv3pvHivGWN3m/XkeP8duYXPPvRlxwvLOaq/j0alfFmtbW1vPTCn/nzi//ivx/MYvHCBaSnpjYoc+nlV/LWjJlMe+99brnzLl556R8AzP30EwDemjGTv738Cq+89E/sdrvH6+BN7JblsVtz8alGev8OSazamw5A6vE8QloEEBkS1KBMZEgQwYEBzkzDqr3p9O+Q5Ng+OYmVe9MAx4fAgOQk53aX9e7MxoOHKSqvdD6WGB2Bn82w64gjC1hZU0NVTa3b6vd9dWgVQ05RCXnFpdTaLTamHqZvu8QGZfq0S2TNgUMAbE7LpGtiHABH8go5Ueb4AnK0oAh/Pxv+NhvVtbXsO5oDQK3d4nBeAdGhwR6s1fc3vEsHvtq2D4A9WdmEBrUgJiykQZmYsBBCAgOcGd+vtu1jRJdkACwLQgMDAUcmLN+Le1K+iwH1zoeDx/MICQxs8nwKCvTn4HFHz4PjvGkDOI6TY4XFjV43I7eAwjJHxjgz/wQB/n7423zqowaAgSltWb7bcQE9cCyXkBYBRIU0PPajQoIJDgxgf13PzPLdqc4vd/UzfEEB/hfFSgQVW3dQW9T4mLhQuOsaVFxeSXpOPrWn/IRiUIA/XVq3ch6HtXa7V2WOk+NiyC4qJre4lFq7nXUHM+jXIalBmX4dkli1z1HnjamH6ZYU73x83cEMaux2cotLyS4qJjkuBoD9R3Moraxq9H67jhxzNphSj+cSHeZb16I9u3aS2KYtiUltCAgIYPyEy1n5zdIGZULDwpz/rigvd35xOZSWyoDBjuRZdEwMYeHh7N29y2OxS/PwqeEu0aHB5JeUOe/nl5QTHRrsbFyeLFNQWq9MabmzURkRHOQse6KsgvBgx4drVGgwA5Lb8NfPv+aeuJbObeMjwymrrOLHV4wkNjyMXZnHmLVmm9debKNCght0pxeUldOhVUzDMqHBFJQ4ytgti/KqakJbBDb4QOzfIYkjeYXUnPItPTgwgN5tW7Nk5wE31uL8iw0PJaeoxHk/t6iEluGhDY6llnXdz84yxSXEhocC8Nqilfzx1kk8cNlwjDH8/L+fOMt1T4rn1ftvIK+kjDe+Wt3kcBFvFR0aQl7986m0jOjQkFPOpxDn8VK/jKsGpbTlUE5Bo2PJF8SEhZBX8u0xkV9SRkxYsPMLiKNMw8+kvJKyBl8AbxrejzHdUyirrOZ3sxd6JnBxG3ddg06nVUQYxeWV3HvJUNq2jOJQbj4zVmzymmRRdGgwBfX2R0FJOSnxMactc/KaExYUSHRoMKn1hu0UlJR/pwTQqG4prD+Y8T1r4Fm52dnExcc777eKi2f3zsbDRz/5aCaz3n+P6uoa/vGf1wDo2LkLK79ZyvgJl5OdfZx9e3aTffw43Xv28lj83sZb22Lnk8vpLWNMkjFmhDFmzMmbOwM7TRCNHmr8f9REmbO87K0j+/PRmq2N/sNtNkPn1q2YuWoLv/t4Ia0iwhjVNfm7xdzcvuMx3DoqgmsH93aOoTzJZgz3jhvK17sOePWY/CY1PiQa/V83UQSrbuddPbAnry9axR0vv8fri1bx2NXjADhwLIc7//0ej0ybxWfrd/DbG688z4G7WVOVPuWAaeKUc/mQSoqO5Obh/fjvsnXfNTKv0PQxcWqhM38mzVy9hUffms3KvWlc0bfr+QxPmoObrkGn42cztG8VzdKd+3lu1gIqq2uY5FVDPM5xf1iubtu0SQN6UGtZrNl/yLUNvITVxJFgmjimrrvxJqbP/pwHH/0/3n17GgBXXTOZVnHxPHT3nfz7H3+nV++++Pn5uT1maV4uZdKNMX8BbgZ2ASe/wlvAN6cp/yDwIMDrr78ORDRVzCXje3ZiTI+OAKRl5zfIUp2a1QIoOCXTFxMaTGFddrmovILIEEcmIzIkyDm+vEOrGB6+bAQAYcGB9GnfGrvdoqC0nIzcQnLqGqWb0zLpGN+S5Y2HDXqFwrKGmYjokGBOnLJ/CkvLia7bbzZjCA4McGbRo0KCefCy4byzbH2DrDI4xkVmFxXztY9k0a8Z2JOJ/bsDsC8rh1YR33YhxkaENciGAeQWlzoz5wCx4WHkFTvKTOjdxTmJ9JvdB/nZpLEADSYdrz+YwaO20UQEB1HkxfMWLu3VmbE9OgGQlp1Hy7AQ9tc9FxMa0mhiY35JWYMu5ZjQEApLG+67pkSHBvOTiaOZung12fV6MbzdhD5dGN+rM+AYztAyLBRwDPeKCWvYqwCQX9wwc94yLKRBFvWklXvT+NXk8cxas819wYtbeOIadDr5JeUUlJSTmu2YsLwh9QhX1X2ueYOC0jKi6+2P6NPtjzDHZ0v9a44r2zZlRJcO9GmXyN/nfn3+KuIhreLiyT7+7ST6nOzjtIyNPW358ROu4MW//AkAP39/fvzzx53PPXr/PbRp2859wfoAre7yrWuBrpZlVZ61JGBZ1lRg6sm7a1794FxiA2DJzgPO4RV92rXm0t6dWXsgg5T4lpRVVjfoZgRHF2JFdTUp8S1JPZ7HiK4d+Gq7oxmyOT2TkV2Tmb95NyO7JrM5LROAJ6bPdW5/7yVD2Xook83pmRhjCG0RQHhQC4orKumeFEd6duPVHbzFoZwC4iLCaBkWQmFZOQNT2vL20oZZzG0ZRxnWqT1p2fn0T05yTvoLDgxgyuUj+WzDDlKzG64ccM3AngQHBDB9ue9MjJyzcSdzNu4EYEindkwe1Iuluw7QLTGOssqqRo30/JIyyqqq6ZYYx56sbC7r04XP1ju6IfNKyujTLpFtGVn065BEVv4J4GS3tuOi0jUxDpvBqxvoAIt37GfxDsf50Ld9Ipf16sKaA4foGN+S8qrTnU81dIxvycHjeYzsmsyi7fvO+B4hgQE8NmkcH63Z6hyr7SsWbdvHorr5C/07JHF5366s2pdOp4RYyiqrGzUiCsvKqaiuplNCLAeO5TK6ewoLtjq+xSdEhTvH7A9MaUNWwQnPVkbOC09cg06nqLyC/NIy57HUIyneq46j9Ox84iPDiQ0PpaC0nCEd2/HG4tUNymxNz2REl2RSj+cxMKUte7KOOx9/4NLhLNq6l6jQYOIjw0k7y/W1Z9sEruzXnb9+vsRrhvx8F9269yDz8GGOZmUS2yqOJYsW8vTv/tCgzJGMDNq0czS+16xcQVJdQ7yiohzLguDgYDasXYOfnx8dUlI8XgfxLFcb6alAAOBSI91dtmUcpU/7RP5829VU1dTw1tdrnc89e+MVPPvRAgDe/WYD944fSqCfP9szstiecRSA+Zt288jlIxndLYW8kjJndvR0LMviw9Vb+MXkSzBAek4By3annnGb5mSvi/fRK0djM4bV+9I5WljE1QN6cCi3gO0ZR1m1L427xw7h2RuvpKyyijfr9uHYHh1pFRHGxH7dmdjPkal5+cvl+NtsTOzXnWOFRTx57WUALNt1gFX70purmt/ZugMZDO7Yjren3EpldQ1/n7vU+dwr99/AlGmzAEd9f3H1JQQG+LHh4GHneMcX5y3jkctH4mczVNXU8uJ8x6oDo7uncPWAntTa7VTW1PKnT77yeN2+j62HsujTLpEXbr+Gyppapi1Z43zu+Zsm8szMLwB4Z9l6Hhg/jEB/P7ZlHGVbRhYAA5PbcMfoQYQHt+CxSWPJyC3kb3O/5rLeXYiPDGfyoF5MHuQYL/nCnCUUlzfrx8d3tjk9k34dknjxrmuprKnh9UWrnM/96bZJPDVjHgBvLVnLwxNGOpZgPJTJlnTH/rllZH8SoxzL5uUUlfJmvf17oUp49kmC+/XBLyqSDrPfI//Ndymat6C5wzpv3HUNiggO4pkbLic4MADLspjQpytPfzCfiuoapi/fyIOXDsfPz0ZOUQlvLVnbOLBmYrcsZqzYyM8mjcVmbKzcm0pWQRE/GNSL9Jx8th7KYvmeVO4fP4w/3jqJ0soq53mUVVDEhtTDPH/zVdgtO9OXb3QOQ3zg0uF0TYwjLKgFf71jMp9v2MGKPancPmog/n7/n737jo+qyv8//jqT3kiBJJAQAqFHpFdpigpWLKu7KGtbFcva/fq1rN9ZeMymAAAgAElEQVRd1+/uftdd19/qugqKu2tDsXekixTpvZcAgQQIkEB6nfP7Y8KQBozCzCTk/Xw88iD33nNnPudw750zn3vuSYB7yGHGgcO8M3+5v6r/owUEBvLAf/03//3AfTidVVx65VV0SOvIvya9Stfu6QwdMZJPP5zKimVLCQwMJCoqiid+93sAjuTm8d8P3odxGFrFJ/DkM//r59r4XzNIpGM8GXhvjPkY6AXMpkZH3Vr7gAfvYX91Gpn0s9G/7hnHvW985O8wGpVXbr8OgDF/nOjnSBqX6b+5m1temeLvMBqVN++9EYAbXnzbz5E0Lu89eBPbho3xdxiNSucFrk6zPoNq+9c947hjotqkpsl3jyP7SNMZmucLSTGRcIKnlxqD5TuzfNZN798h2S/t4Gkm/YvqHxERERERv2oOs7t41Em31r5pjAkGulSv2mKtbTyTtYqIiIiInEU8nd3lfOBNYBeuWx8pxphbrLUNzu4iIiIiIuItmt3luL8Bo621WwCMMV2A94B+3gpMRERERKS58rSTHnSsgw5grd1qjAnyUkwiIiIiIiekMenHLTfGvAEcm05hPNB0Js0WEREREWlCPO2k3wP8GngA15j074FXvBWUiIiIiEhz5unsLmXAC9U/IiIiIiJ+0wxGu5y8k26M+cBa+3NjzDqgXnNYa3t6LTIRERERkWbqVJn0B6v/vcLbgYiIiIiIeKI5TMHoONlGa+2+6l/vtdburvkD3Ov98EREREREmp+TdtJruLiBdZeeyUBERERERDxhrfXZj7+cakz6Pbgy5mnGmLU1NkUBC70ZmIiIiIhIc3WqMelTgGnA/wFP1FhfYK3N9VpUIiIiIiIn0BzGpJ+0k26tPQocBW4AMMYkAKFApDEm0lqb6f0QRURERESaF4/mSTfGXIlrjvQkIAdIBTYB53gvNBERERGR+ppDJt3TB0f/AAwGtlprOwAXojHpIiIiIiJe4VEmHaiw1h42xjiMMQ5r7VxjzHNejUxEREREpAH+nHXFVzztpB8xxkQC3wPvGmNygErvhSUiIiIi0nx52km/CigBHgbGA9HAs94KSkRERETkRJRJB4wxAcDn1tqLACfwptejEhERERFpxk7ZSbfWVhljio0x0dVTMoqIiIiI+I3z7E+kezzcpRRYZ4yZCRQdW2mtfcArUYmIiIiINGOedtK/rv4REREREREv86iTbq3VOHQRERERaRT04Gg1Y8xOoF5rWGvTznhEIiIiIiLNnPHkm4gxpmWNxVDgeiDOWvtbD97j7P+qIyIiInL2Mf4O4ERmrN3qs/7l6J5d/NIOng53OVxn1d+NMQsATzrpjH/p7R8b11nt3Qdu4o6J7/s7jEZl8t3jALj4D6/6OZLGZebT93D+My/7O4xG5btn7gPgov99xc+RNC6z/udefvWqris1/ese13Vl27Axfo6kcem8YDoFBQX+DqNRiYqK4k+fzvJ3GI3KU9dc5O8Qmj1Ph7v0rbHoAPoDUV6JSERERETkJJzNYKCGp7O7/I3jw1YqgV24hryIiIiIiMgZdtJOujHmkepfv8LVST82JscCVwAveC80EREREZH6NLvL8SEtXYEBwOe4OupXAt97MS4RERERkWbrpJ10a+3vAYwxM4C+1tqC6uVngA+9Hp2IiIiISB3Osz+RjsPDcu2A8hrL5UD7Mx6NiIiIiIh4/ODo28BSY8ynuMajXwPor5CKiIiIiM85m0Eq3dN50v9ojJkGDK9edZu1dpX3whIRERERab48zaRjrV0JrPRiLCIiIiIip9QcZnfxdEy6iIiIiIj4iMeZdBERERGRxkCZdBERERER8Tl10kVEREREGhkNdxERERGRJsWJhruIiIiIiIiPKZMuIiIiIk2KHhwVERERERGfUyZdRERERJqUZpBIVyZdRERERKSxUSZdRERERJoUZzNIpSuTLiIiIiLSyCiTLiIiIiJNimZ3ERERERERn1MmXURERESalOaQSW/SnfSbRwygV/skyiurmDRzEbsO5tYr0z4+jrsvPo+gwADW7Mrmre+XAXDD0L707dCWSqeTA0cLeG3mIorLK9z7tYwM5y+/HMvHS9byzaqNPqvTT3FOSmtuGNoXhzHM35TBtNWbam0PdDi4fdRgUuNjKSwtZ9KsRRwuKALg0j7dGd4tDae1vLdgJRv27icwwMHjV11IoMOBw+FgRcYevli+vtZr3jC0L0O7deC+Nz72WT1Px72jhzKwUyplFZX89cs5bN9/qF6Zzq1b8djYUQQHBrJ0+25embEQgI6JLXnw0pEEBwZQ5XTy0rfz2ZKdQ2RoMI9ecQFJsdGUV1byt6++a/AYbCruv3Q4gzunUlpRyZ8/m822fQfrlbl91GDG9OpKVFgIl/7pNff6nqlJ3HfJMDomtuLZj6Yzb+MOX4Z+Rv16zDD3sfKXL2af4FiJ57+vOn6s/HP6AgDSElvy0GUjCQsOYv+RAv7v05kUl1fQNSmBhy8/HwBj4K15y1i4Zacvq/Wj3Ti0L+emtqG8soo35iwh81BevTKprWK5fdQgggIDWLd7H1MWrgQgIiSYuy8+j1ZRERwqKOLVGQspLq+gdUwUv7pgEKnxsXyyZC3T12xxv1ZYcBC3nT+Q5LhoLJZ/z13KjgOHfVZfb0l48hEizhtEVd4RMm++y9/h+NSiRYt4/vnncTqdXH311dx6660Nlps1axZPPPEEb731Funp6WRnZ3P99deTmpoKQI8ePXjqqad8GLlvpCW05OKeXTDGsGZ3Fj9s3V1re0rLGC7u2YWEFpF8tmw9m7Nz/BSpeMIYcwnwIhAATLbW/rnO9keAO4BK4CDwK2vt7novVEOTHe7SKzWJ1jFRPPrW57wxZzG3XTCowXK/umAQk+cs5tG3Pqd1TBS9UpMAWL9nH4+/+yVPTvmK/Xn5jO3fo9Z+vxzRnzW7s71ej9NljGH8sP78/et5/M/UaQzs1I42sS1qlRnWPY2isnKeeu9rZq7dwnWDegHQJrYFAzu247dTp/H3r+cxfnh/jDFUVjl5/ou5/P6j6Tz70bf0SGlDWkJL9+ulxscSHhLs03qejoEd25EcF8Otr0zh79/M44FLRzRY7oFLR/D/vp7Hra9MITkuhgEd2wFw54VDeHv+cu6e/CFvzlvGnRcOBuCGof3YceAwd73+AX/5Yg73jh7qszqdaYM6p9I2LobxL73D376cy8OXj2yw3A9bd3L36x/WW59ztIA/fzabWeu2ejtUrxrYqR3JcdHc8s93+X9ff8eDlzXcDg9eNoIXvvqOW/75Lslx0e5j5dErLmDy7MXcOWkqCzdn8PPz+gCwKyeXeyd/yN2vf8CTU77ioctH4jDGZ/X6sc5t14bEmEienPI1b85bxs0j+jdY7qYR/Xlz3jKenPI1iTGRnNuuDQCX9enOpqwDPPne12zKOsBlfdMBKCorZ8qClUxfvbnea904rC/r9uzjN+9/w+8+mE52Xr73KuhD+d/MIPvR3/g7DJ+rqqriueee46WXXuLDDz9k+vTpZGRk1CtXVFTE1KlT6dGj9mdwcnIyU6ZMYcqUKWdlB90AY3p1Zeqi1bw26wfS27amVVRErTL5JaV8uWIjG/Ye8E+QjZzTWp/9nIoxJgD4J3ApkA7cYIxJr1NsFdDfWtsT+Aj4y6let8l20vulpTB/s+uE377/EOEhQcSEh9UqExMeRlhwkDsTNn9zBv3SUgBYl7nP3fDb9x8iLjKi1mvnHC1kb+4RX1TltHRIiCMnv4BDBUVUOZ0s3ZFJ7/bJtcr0bp/Moq2urN2KjD10S050r1+6I5NKp5NDBUXk5BfQISEOgLLKSgACHA4CHAaLq62MMVw/uDcfLV7tqyqetiFd2zNrnStjtynrAJGhIcRFhtcqExcZTnhIMJuyXBfDWeu2cF7X9oDrllp4SBAAEaHBHC4oBlxZxFW79gKw5/AREmOiiImofQw2FUO7dmD6GlfHaePehtvo2LbcwuJ66/cfKSDjwOEmf/vxvC4dmLm25rESfMpjZebaLQzt2gGAti1jWJvp+nK/YudehndLA1zn07HrTXBgADTyZurTPplFW3YBkHHgMOEhQUSHh9YqEx0eSlhwkDvbvWjLLvpUX3v6dEh23ylYuGUnfTu41heUlLHrYC5VztoNEBoUSJc28czf5LqmVzmdlNS4s9mUla5ZT1V+gb/D8LkNGzaQkpJC27ZtCQoKYvTo0cybN69euYkTJ3LzzTcTHNx0Ej9nQlJcNHlFJRwpLsFpLRv3HqBzm/haZY4Wl3Iwv7DJX1ebiYHAdmtthrW2HHgfuKpmAWvtXGvtsQ/QxUDbU72oR510Y0yEMcZR/XsXY8xYY0zQjwr/DIuLDHcP2QDILSwmNrJ2Byk2MqxWhyK3sLjBjsfIczqxZncWACGBgVzZ7xw+WbrWS5GfWbERYeTVqGNeYQmxdTqKNcs4raWkvILI0OCT7muM4bfXjeGFW65m494D7MxxDeMY1aMza3ZncbS41NtVO2NaRUWQk1/oXj6UX1gvY3HstvwxB/OL3GVenbGQCRcO4d0HbmLChUN4Y+5iADJyDjOsq6sT1jUpgcToKOLrvG5TEd8ikoM12uhgfiHxLSL9GJF/tIqKqNMORQ0fKycosysnl/O6tAdgRPeOtdqwW1ICk+8ex+t3jePv38xr1HP8xkbUvXae4LpSVKNM0fEyLcJC3deIo8WlRIXV7uDXFd8ikoKSMn51wSB+d90Ybj1/gOvLjDRZOTk5JCYmupcTEhLIyak9XGPz5s3s37+f4cOH19s/OzubG2+8kQkTJrBq1Sqvx+trUaEh5Jcc/xwtKCklKjTEjxE1PY0pkw4kA3tqLO+tXncitwPTTvWinmbSvwdCjTHJwGzgNuA/Hu7rM3Xb0VD/dnLdpr6qfw+qnE531udng3sybfUmyioqvRTlmdZAHesdTycqc+J9rbU8+9F0Hnv7CzokxJEUG010eCj901KYvW7baUftSw0eBx6cdMeKXNHvHF6duYjxL73NqzMX8egVFwDw/sKVRIWFMPGO67l6QA+27z9UL0PYlDXH7I1pYAjKj7muPP/lHMb2P5dX7riO8JAgKquc7jKbs3O4Y+L7/PqNj7hhaF+CAhpxJ9SDdmjw+vET3y7AYUiNj+W7Ddv4/UfTKauo5PI+de8US1NX8/xyOp288MILPPzww/XKtWrViq+++oopU6bw8MMP8/TTT1NYWFivnIivGGMmGGOW1/iZULdIA7s1eEk0xvwS6A/89VTv6+mDo8ZaW2yMuR34h7X2L8aYE361rQ5+AsCkSZOAMzME4OKeXbjgnM6A6xZsy6gIqH64LS4ynCNFJbXK182cx0WG18ocD++WRp8ObfnTpzPd6zomtmJgp1RuGNqX8JBgrLVUVFW5b4E3NnlFxcTWqGNsZBhHiksaLJNXVILDGMKCgygqK/do35LyCrZk59CjXWv25eWTEB3Jn268AoDgwED+dMPlPPXe116s4U8ztt85XFb9Ib9lXw4JLSLZUL2tVYtIDtcZsnGooHbGNL5FBIcLXZn10T27uh8i/X7TDh654nwAissreP7Lue593r5vPPuPNJ1xtFcPOJcr+rnaaHNWTq2sb3yLyFp3Fs5mY/v3cB8rW7PrtsPx4+CYgwWFtKpbprqt9hw+whNTvgQgOS6aQZ1S671f5qE8Sisq6ZAQx9YGHs71l1HndGJEekcAdubk1rl2nuC6ElGjTESY+xqcX1JKdLgrmx4dHkpBycnvvOUWlpBXWEJG9R275Rl7uaxP9zNSL/GPhIQEDhw4PpY6JyeH+PjjwzmKi4vZsWMHd93lepj28OHDPPLII7zwwgukp6e7h790796d5ORkMjMzSU8/e764FZSW0aLGHaaosFAKSsv8GJGcjLX2NeC1kxTZC6TUWG4L1Huw0RhzEfAbYKS19pT/4R530o0xQ4DxuFL0J923TmXsvJfe9vBtTm7m2q3MXOt6MK13+2RG9+zKD1t30al1K0rKKup9iBwpLqGkooJOrVuxff8hhndLc4+77ZmaxJX9z+F/P55BeWWVe5///XiG+/drB/WktLyy0XbQwXV7PTE6ilZREeQVlTCwYzten/1DrTJrdmVxXpcOZBw4TL+0FDZnH3Cvv/PCIcxcs4WYiDASo6PYmZNLZGiIe0xoUEAA3du25ttVm1iXuY9H3/rc/bov3/6zRtlBB/hixQa+WOHqlg/s1I6r+p/L3A3b6Z6cSFFpWb1x1bmFxZSUV9A9OZFNWQe46NyufL5sHQCHC4vpmZrE2t3Z9GmfTFbuUcA1g0VZRSWVTieX9unOusx9tWYIauw+W7aOz6rrOLhzKtcM7Mmc9dtIb5tIUVl5g2PPz0ZfLF/vnr1oUKdUrhrQo8axUr8d6h4rF/fs6m7HmHBXZ9YAvxzen6+qj8HWMVHkHC3EaS0J0ZG0bRnD/iONa5zynA3bmbNhOwA927XhwnM7s2R7JmmJLSkuq6g3xO1ocSmlFRWkJbYk48BhzuvanlnVd9lW7cpiaNcOfLNqE0O7dmDVzqyTvnd+SSm5RcW0joli/5EC0pMTyc476p2Kik+kp6ezZ88esrKySEhIYMaMGfzhD39wb4+MjGT27Nnu5QkTJvDQQw+Rnp5OXl4eLVq0ICAggL1797Jnzx6Sk082cqDpyc7LJzYyrPpLbBnpbRP5fNn6U+8obo3sbu8yoLMxpgOQBYwDbqxZwBjTB5gEXGKt9WiqHk876Q8CTwKfWms3GGPSgLmn2MerVu/Konf7ZF645WrKKyqZNGuRe1vN7O6/5y7hrouHEhwYwJpdWe4ZW24ZOYCggACevPoiwPXw6L/mLvF9RU6T01qmLFhRPVuEg4VbMsjOy+eq/j3YdTCXNbuzmb85gztGDeZPN1xOUVk5k2a62io7L5/lGXt49heX4bRO3p2/AmstMeGh/GrUYBzGYAws27HH/TBcU7R0eyaDOqXy5q9vpKyislb2e+Id13P3ZNdsJS9N+57/unIUIUEBLNueydIdmQC88PV33Dt6GAEOQ3llFX//+jsA2rWK5fGrRlHltGQeyuNvX/n1lDgti7ftZlDnVN594CbKKip57vPjH56T7/4Fd0ycCsBdF5/HRed2ISQoiA8fuZWvV27kP98tpWtSAn8YdxmRoSEM6dKBW88fyG2vvOev6vxkS7bvZmCndrz16/GUVVby1y/muLdNvPPn3P36BwC8+M08Hhs7ipDAQJbuyGTpdtexckGPzlxVPVPUgs0ZfFudFOiR0oZx4/pSWeXEWstL076vNR61sVmbuY+eqUn8+cYrKK+srHVtfOb6MTzz4XQA3v5+Ob8aNYjggEDWZWazLnMfAN+s3MQ9o4cyvFsahwuLebX6TlSLsFB+e91owoKDsNZycc+uPP3+N5RWVPLu/BVMuHAIAQEODuYX8q85Te963JDWzzxBWO+eBMRE0/6Td8h9423yv57u77C8LjAwkMcee4z777+fqqoqxo4dS8eOHZk4cSLdu3dn5MiGZ04CWLlyJZMmTSIgIACHw8GTTz5JdHS0D6P3PmstM9ZsYdzQPjgwrNmdzaGCIkZ0T2NfXj7b9h+iTUwLfja4J6FBQXRq04rh3dN4ffZif4cuDbDWVhpj7gOm45qC8V/V/eVngeXW2i9wDW+JBD6sHvqVaa0de7LXNZ58EzHG9LDW/tSveHb8Gcqkny3efeAm7pj4vr/DaFQm3z0OgIv/8KqfI2lcZj59D+c/87K/w2hUvnvmPgAu+t9X/BxJ4zLrf+7lV6/qulLTv+5xXVe2DRvj50gal84LplNQ0Lju5PhbVFQUf/p0lr/DaFSeuuYiaHisdaPw1vwVPkul3zy8n1/awdMHRycaY5YaY+41xsR4NSIRERERkWbOo+Eu1tphxpguuGZ1WW6MWQr8x1o74xS7ioiIiIicUY1sTLpXePzHjKy1W4GngceBkcCLxpjNxphrvRWciIiIiEhz5FEm3RjTE1cW/XJgJnCltXalMSYJ+AH4xHshioiIiIgc1xwy6Z7O7vIy8DrwlLXWPc+htTbbGPO0VyITEREREWmmPB2TPuIk2zR1i4iIiIj4jFOZdBdjTGfg/4B0wP0nsqy1aV6KS0RERESk2fJ0uMu/gd8B/w+4ANf49EY7d6aIiIiInL2aQSLd49ldwqy1s3H98aPd1tpngFHeC0tEREREpPnyNJNeaoxxANuq/+xpFpDgvbBERERERBrWHGZ38TST/hAQDjwA9ANuAm7xVlAiIiIiIs2Zp7O7LKv+tRDXeHQREREREb9o9rO7GGO+BE7YCtbasWc8IhERERGRZu5UmfTnq/+9FmgNvFO9fAOwy0sxiYiIiIg0ayftpFtr5wEYY/63zh80+tIY871XIxMRERERaYAeHD0u3hjj/sNFxpgOQLx3QhIRERERad48nYLxYeA7Y0wGrjHqHYC7vBaViIiIiMgJNIcHRz3NpH8HTALycHXSJwHzvBSTiIiIiEiz5mkm/S0gH3ipevkG4G3gem8EJSIiIiJyIs0hk+5pJ72rtbZXjeW5xpg13ghIRERERKS587STvsoYM9hauxjAGDMIWOi9sEREREREGtYcZnfxtJM+CLjZGJNZvdwO2GSMWQdYa21Pr0QnIiIiItIMedpJv8SrUYiIiIiIeKgZJNI966Rba3d7OxAREREREXHxNJMuIiIiItIoNIfZXYwPBt6f/a0oIiIicvYx/g7gRF6cNt9n/csHLx3ul3ZQJl1EREREmhTN7nKG3DHxfV+8TZMx+e5x3P36h/4Oo1GZeKfr72KN+/tbfo6kcXn/oZv5xd/f9HcYjcrUh24B4Nq//dvPkTQunzx6m661dUy+exwABQUFfo6kcYmKimLbsDH+DqNR6bxgOs9+PMPfYTQqv/3ZaH+H0Owpky4iIiIiTUpzyKQ7/B2AiIiIiIjUpk66iIiIiEgjo+EuIiIiItKkNIcpGJVJFxERERFpZJRJFxEREZEm5ezPoyuTLiIiIiLS6CiTLiIiIiJNisaki4iIiIiIzymTLiIiIiJNiv6YkYiIiIiI+Jwy6SIiIiLSpDidyqSLiIiIiIiPKZMuIiIiIk2KxqSLiIiIiIjPKZMuIiIiIk2K5kkXERERERGfUyZdRERERJqUsz+Prky6iIiIiEijo0y6iIiIiDQpmt1FRERERER8Tp10EREREZFGRsNdRERERKRJaQ5TMDapTvo5Ka25YWhfHMYwf1MG01ZvqrU90OHg9lGDSY2PpbC0nEmzFnG4oAiAS/t0Z3i3NJzW8t6ClWzYux+AW88fSM/UJApKSvndB9+6X+uqAefSp30yTmspKCnjX3MXc7S41HeV/YnS2yby8yF9cBjDwi0ZTF+zpdb2QIeDW88fSLtWsRSVlTF59mIOFxYTERLMhIuGkBofx+Ktu3h/0SoAQoIC+a8rL3DvHxsRxpJtu/lw8Rqf1ut03TJyAH06JFNWUcWrMxay62BuvTIdEuK4Z/RQggMDWLUzizfnLQPg50N60y8tBYslv7iUV2csJK+ohH5pKfx8SG8sliqnk7fmLWdLdo6vq3Zabh05sLpdKnl1xkJ2nqBd7h09zN0u/5m3FHC1S/+0FCxwtLiUV2csIK+oBHAdh7eMHEiAw0FBSSm//2i6L6t1Wm6/YBB9O7SlrLKSl79dQEbO4Xpl0hJacv8lwwkODGDlzr28MXcJAL8Y0puLzu1CfonrWvHugpWs3LmXXqlJ/HJ4PwIdAVQ6q3hz3nLW79nn03r9GL681l43uBe9UpOpcjrJyS/k33OXUFJe4bvKngGLFi3i+eefx+l0cvXVV3Prrbc2WG7WrFk88cQTvPXWW6Snp5Odnc31119PamoqAD169OCpp57yYeT+k/DkI0ScN4iqvCNk3nyXv8Pxqo6JLRnTqxsOY1i1cy8Lt+6qtT3AYbi6/7m0iW1BSXkFHy1Zw9HiUhzGcEXfdNrEtsBamL5mM7sP5QFw49C+RIaG4HAYMg/lMW3VpmYx20lz02Q66cYYxg/rzwtfzSWvqISnr72Y1buz2JeX7y4zrHsaRWXlPPXe1wzo2I7rBvVi0qxFtIltwcCO7fjt1GnERITxyBUX8Jv3v8Zay8ItO5mzfhu3jxpU6/2mr97E58vWAXBhj85c2a8H78xf7tM6/1jGwA1D+/LiN9+TV1TMk1dfxNrd2ew7UuAuM7RrB4rLy/ntB9Pon5bCNQN7MnnOYiqqqvhi+XqS4qJJjo12ly+rqOSPn8x0Lz959UWs2pXl03qdrt7tk2kT24KH/vMZnVq34o4LB/H0+9Pqlbt91GBen/0D2/Yd4omrL6R3+yRW78rmyxUb+OCH1QBc0rsb1w7qyRtzlrB+zz5WZOwBoF2rGB68bCSPvvW5T+t2Onq3T6Z1bBQP/udTOrduxe0XDubp97+pV+6OUUN4bfYPbNt3sLpdklm9K6teu/xsUC8mz1lMeEgQt18wmD99NovDBUW0CAv1ddV+sr4d2tImtgW//tfHdGkTz4SLhvDElK/qlbvroiG8OnMhW/cd5OlrL6ZP+2T3efHVyo18vnx9rfL5JaX86dNZ5BWV0K5lDP/zs9Hc+doHPqnTj+Xra+3GvQf4ZMlanNbys0G9uKxPOh8vaTpJgKqqKp577jn++c9/kpiYyM0338yIESNIS0urVa6oqIipU6fSo0ePWuuTk5OZMmWKL0NuFPK/mcHRj78g8enH/B2KVxng0t7deWfBCvKLS7lj1GC27DvIoeovtQB92relpKKCl6cv4Jy2rbmoRxc+XrqWvh3aAjBp1g+EhwRz49C+TJ6zGICPlqyhvLIKgOsH9yK9bWv3F+LmQg+ONiIdEuLIyS/gUEERVU4nS3dk0rt9cq0yvdsns2jrTgBWZOyhW3Kie/3SHZlUOp0cKigiJ7+ADglxAGzbdzvyRQUAACAASURBVJCisvJ671daUen+PTgokKYwI2f7+Dhy8gur28iybMceeqbWbqOe7ZP4ofpb/Mqde+mWnABAeWUVOw4cprL6pG9IQotIosJC2L7/kNfq4A39O6bw/aYdAGzff4jw4GBiwsNqlYkJDyMsOIht+1x1+37TDvp3bAdQK6sXEnT8e21ZjWMkpIkcIzUN6JjC95syANi2/xARJ22XgwB8vymDAR1TgNrtEhoU6K79sK5pLN2e6c6sHssqNwUDO7bju43bAdi67yARIcHERtRuk9iIMMJCgtha3SbfbdzOoE6pJ33dnTm57rsMmYePEBwYQGBA47z8+vpau3Hvfvdt64wDh4iNDKtXpjHbsGEDKSkptG3blqCgIEaPHs28efPqlZs4cSI333wzwcHBfoiy8Slds56q/IJTF2zikuOiySsq5khRCU5r2bB3P12TEmqV6ZoUz9rd2QBszDrgPmfiW0SwM8d1d7O4rJyyigqSYlsAuDvoDmMIcDiwTezzRzzjUSbdGBMBlFhrncaYLkA3YJq11mf3JGMjwsgrLHYv5xWWkJYYd8IyTmspKa8gMtT1IZtx4HCtfet+8DbkmoHnMqRLB0rKy/nrF3PPUE28p24bHSkqpkNCy1plYsLD3J2FY20UERLc4IdnXf07prgzx01JXEQ4hwuOt0tuYTFxkeEcKS45XiYynNwabZdbUExcRLh7+Rfn9WZE944Ul5Xz7Mcz3OsHdExh3NC+RIeH8tzns71ckzMrNiLc3ZEGOHzCdjleJregiNha7dKHEd07UlJWzu8/dg1paRPbggCHg99eN4awoCCmrd7o/jLQ2MVFhtfKcB0uKCIuMtx9zhwrU/N4OlzgardjLu3djZHpHdlx4BD/+W5ZvXNrSOdUMnJyqaxyerEmP50/rrXHDOuWxrIdmadZA9/KyckhMTHRvZyQkMD69bXvpGzevJn9+/czfPhw3n777VrbsrOzufHGG4mMjOSee+6hT58+PolbfCMqLLTWUNn8klKS46JrlwkN5Wh1MsNaS2lFJWHBQRw4WkDXpHjW791PdFgobWJa0CIslOzqu1rjh/UlKTaa7fsPsWnvAd9VqpFoDmPSPU3lfA+EGmOSgdnAbcB/vBVUw0y9NfX/f05UxpN96/t06Tr++50vWLxtN6N6dPYoSv9qoJ51vl2b+kU8NqBjuyb3AQo01Cz1sw6nKDN10Wp+/cbHLNiykzG9urnXL9uxh0ff+pznv5zLz4c0rQ9X08DB8GOPl6mLVvHrNz5iwZYMLqluF4dxkJbQkuc+m82fPp3JtQN70SamxRmL25saqm/da4U5yXn27ZrN3PvGxzz61ufkFZZw6/kDapVLaRnDTSP6M3HmojMW85nn+2stwOV906mylsXbdnu2QyNW89xyOp288MILPPzww/XKtWrViq+++oopU6bw8MMP8/TTT1NYWOjLUMUf6p4TJ7jOrtqVTX5JGXeOGsSYXl3Zk3ukVsf03QUreeHreQQGONzZdzm7eNpJN9baYuBa4B/W2muA9BMWNmaCMWa5MWb5a6+9dibiJK+omNga2arYyLBaGb+6ZRzGEBYcRFFZuUf7nsySbbvpl9b2NGvgfXXrGRMRzpGi0jpljme2arbRqSTHRVc/oHLkzAbtJaN7duXP46/gz+OvIK+whJZRx9slLjKcvMLa//+5dbKhcVG1s6fHLNy8k0Gd2tVbvzkrh8ToSKJCQ85gLc680T278tz4K3lu/JXkFRbTMirCva1lA+3iyhIfLxMXFUFeUTF1Ldi80z3kI7ewiNW7syirrKSgtIxNWQdIjY/1Uo1O3yW9u/G3m8byt5vGkltYTKuabdJAfQ8XFtU6nlpGHW+3o8WlOK2ryz5z3VY6t44/Xi4ynMfHjuKlafM5cLTx3ub3x7X2vC7t6dkuicmzfzhDtfCdhIQEDhw4nsXMyckhPv74/3txcTE7duzgrrvu4sorr2T9+vU88sgjbNy4keDgYGJiYgDo3r07ycnJZGY2wUSInFBBSSnR4cefy2kRFkpBaVn9MtXP7hhjCA0KpKS8AmstM9Zu4bXZi5n6w2pCg4Jq3fEFqHI62ZJ9kC51htA0B9b67sdfPO6kG2OGAOOBr6vXnXCojLX2NWttf2tt/wkTJpxujADsysklMTqKVlERBDgcDOzYjjV1HmBcsyuL87p0AKBfWgqbsw+41w/s2I5Ah4NWUREkRke5x3mdSEJ0pPv33u2T2ZfXeD9Uj9l9MI+EFpG0jAonwGEY0DGFtZnZtcqs3Z3NkC7tAddDcp7ORjKgYzuWbW86Hx4z1m7hiXe/4ol3v2L5jkxGdO8IQKfWrSgur6jXcThSXEJpeQWdWrcCYET3jizf4Rra0zomyl2uX1qK+1ZjYvTx9e3j4wgMCKh38W1sZqzdwuPvfsnj737Jsh2ZjOjuerit8ynapbO7XdJY1kC79E9LISvvKADLd+yhW1IiDmMIDgygc+tWZOUe9UX1fpJvV2/m0be/4NG3v2Dp9kzOT+8EQJc28RSXldf7spZX5GqTLm1cHbHz0zuxtPoOU82hHYM6tSOzeiaG8JBgfnPNxbyzYAWbG/kMQL6+1p6T0ppLenfnH9/Od4+zbUrS09PZs2cPWVlZVFRUMGPGDEaMGOHeHhkZyezZs/nyyy/58ssv6dGjBy+88ALp6enk5eVRVeWq8969e9mzZw/JyckneitpgrLy8omLDCcmPAyHMZzTtjVb61wDtmQfpGdqEgDpyYnuWbYCAxwEBQQAkJYQh9NpOVRQRFBAAJGhrmcbjDF0bt2q1tBFOXt4OrvLQ8CTwKfW2g3GmDTAp4O0ndYyZcEKHrp8JA7jYOGWDLLz8rmqfw92Hcxlze5s5m/O4I5Rg/nTDZdTVFbOpOpbytl5+SzP2MOzv7gMp3Xy7vwV7qeC77xwCF2TEogMDeEvvxzLF8vXs2BzBj8b1IvWMVFY6xqX+nYjn9kFXG00ddEqHrh0BA5jWLRlJ/vy8rmy3znsPpjL2sx9LNyyk9vOH8izP7+U4rJy95PiAH8cdxmhQUEEBDjolZrES9O+d88M0y+tLS9/u8BfVTstq3Zl0btDMi/eeg1llZVMnHF8qMGfx1/BE++6Zu94Y84S7hl9HsGBgazelcXq6o7JDUP7khTbAqeFQwWFTJ7tarNBndsxvHtHqpxOyiurePGb731fudOwalcWfTq05cVbr6W80jUF4zHPjb+Sx9/9EoDJcxZz7+ihBNVplxuH9qtuF9cHx+vV7ZKVd5Q1u7P46y/HYq1lzoZt7DncNO7ArNi5l75pbXnl9p9RVlHFy9Pnu7f97aaxPPr2F4BrtoXjUzBmsXLnXgBuGtGfDvEtsVgO5he6h7Vc1rs7rWOjuH5wL64f3AuAZz+a4R6H2pj4+lo7flg/AgMCeOSK8wHIOHC40c+kVVNgYCCPPfYY999/P1VVVYwdO5aOHTsyceJEunfvzsiRI0+478qVK5k0aRIBAQE4HA6efPJJoqOjT1j+bNL6mScI692TgJho2n/yDrlvvE3+101nqlZPWWuZtnoz44f1xRjD6l1ZHCwo4vz0jmTn5bN130FW7crimgE9uG/MMErKK/h46VoAIkKCGT+sH9ZaCkrL+Gy5a8a54MAAfjGkD4EBDowx7MrJZXnGXn9W0y+aw+wuxgeVtHdMfN/b79GkTL57HHe//qG/w2hUJt55PQDj/v6WnyNpXN5/6GZ+8fc3/R1GozL1oVsAuPZv//ZzJI3LJ4/ehq61tU2+exwABQWN/06oL0VFRbFt2Bh/h9GodF4wvdakAAK//dloOOGIef/7zfvf+KyX/sdxl/mlHTyd3WUuDcwvZ60ddcYjEhERERE5ieYwu4unw13+q8bvocDPgMoTlBURERERkdPgUSfdWruizqqFxpj6f61BRERERMTLlEmvZoypOQGnA+gHtPZKRCIiIiIizZynw11W4BqTbnANc9kJ3O6toERERERETqQ5zO7i6XCXDt4OREREREREXDwd7hIE3AMc+wsN3wGTrLUVXopLRERERKTZ8nS4y6tAEPBK9fJN1evu8EZQIiIiIiInouEuxw2w1vaqsTzHGLPGGwGJiIiIiDR3nnbSq4wxHa21OwCMMWlAlffCEhERERFpmPPsT6R73El/DJhrjMnANcNLKnCb16ISEREREWnGPJ3dZbYxpjPQFVcnfbO1tsyrkYmIiIiINKDZj0k3xoyy1s4xxlxbZ1NHYwzW2k+8GJuIiIiISLN0qkz6SGAOcGUD2yygTrqIiIiI+FSzz6Rba39X/esd1lo9KCoiIiIi4gOePji60xjzLTAVmGObw9cXEREREWmUnM2gK+rwsFxXYBbwa1wd9peNMcO8F5aIiIiISPPl6ewuJcAHwAfGmFjgRWAeEODF2ERERERE6mkOgzo8zaRjjBlpjHkFWAmEAj/3WlQiIiIiIs2YR5l0Y8xOYDWubPpj1toir0YlIiIiInIC+oujx/Wy1uZ7NRIREREREQE876S3NsZ8CiRaa3sYY3oCY621f/BibCIiIiIi9Tit098heJ2nY9JfB54EKgCstWuBcd4KSkRERESkOfO0kx5urV1aZ13lmQ5GREREREQ8H+5yyBjTEbAAxpjrgH1ei0pERERE5ASawQyMHnfSfw28BnQzxmQBO4HxXotKRERERKQZM6eaDN4Y4wCus9Z+YIyJABzW2oIf8R7N4LuOiIiIyFnH+DuAE7n79Q991r+ceOf1fmmHU2bSrbVOY8x9wAc/dX70e9/46KfsdtZ65fbreOydL/wdRqPy11+OBeDyP7/m50gal6+fmMA9k3X+1PTqHdcBcMOLb/s5ksblvQdvIvtIob/DaFSSYiIB+NOns/wcSePy1DUX8ezHM/wdRqPy25+NZtuwMf4Oo1HpvGC6v0No9jwd7jLTGPNfwFTA3VG31uZ6JSoRERERkRNwNoNB6Z7O7vIr4F5gHrC8xo+IiIiISLNmjLnEGLPFGLPdGPNEA9tDjDFTq7cvMca0P9VreppJT8fVSR+Ga4z5fGCi56GLiIiIiJwZp3qm0peMMQHAP4GLgb3AMmPMF9bajTWK3Q7kWWs7GWPGAc8BvzjZ63qaSX8T6A68BPyj+vc3f1wVRERERETOOgOB7dbaDGttOfA+cFWdMldxvO/8EXChMeakD6R6mknvaq3tVWN5rjFmjYf7ioiIiIicMY0pkw4kA3tqLO8FBp2ojLW20hhzFGgJHDrRi3qaSV9ljBl8bMEYMwhY6OG+IiIiIiJNkjFmgjFmeY2fCXWLNLBb3W8RnpSpxdNM+iDgZmNMZvVyO2CTMWYdYK21PT18HRERERGR0+L0YSLdWvsarj/qeSJ7gZQay22B7BOU2WuMCQSigZPOkuhpJ/0SD8uJiIiIiDQny4DOxpgOQBYwDrixTpkvgFuAH4DrgDn2FGN2POqkW2t3/+hwRURERES8oDGNSa8eY34fMB0IAP5lrd1gjHkWWG6t/QJ4A3jbGLMdVwZ93Kle19NMuoiIiIiINMBa+w3wTZ11v63xeylw/Y95TXXSRURERKRJcZ78mcuzgqezu4iIiIiIiI+oky4iIiIi0shouIuIiIiINCmN6cFRb1EmXURERESkkVEmXURERESaFKcv/5qRnyiTLiIiIiLSyCiTLiIiIiJNisaki4iIiIiIzymTLiIiIiJNSjMYkq5MuoiIiIhIY6NMuoiIiIg0KRqTLiIiIiIiPqdMuoiIiIg0KZazP5PeZDvp6cmJXD+4N8ZhWLRlJzPWbqm1PdDh4JaRA0hpFUtRaTlvzF1MbmEx3ZISuHrAuQQ4HFQ5nXyydC1b9x0E4KHLRhIdFkp5VRUA//h2PoWlZT6v2+no2iaesQPOxWEMS7fvZu6G7bW2BzgcjDuvD21bxlBcVs4785eTV1RCbEQYj105ioP5hQDsPpTHJ0vXAnDHqMFEhYXgMIadObl8umwtTe0u010XnUf/jimUVVTy/77+jh0HDtcr0ymxFQ9ffj7BQQEs37GHSbMWAfD4VRfSNi4agIjQEIpKy7j/35/QpU08918y3LWzMUxZsIIftu7yVZVOW3rbRH4+uDfGGBae6Bw6fwDtWsZSVFbO5DmucygiJJg7LxxManwci7fuYuoPq9379EtryyW9u+EwhvV79vPp0nW+rtZpu2XkAHq3T6K8sopXZyxi18HcemU6JMRx98XnERwYwOpd2bw5bxkANw7rS98ObalyOjlwpICJMxdRXF5BgMMw4cIhtE+II8DhYP6mDD5fvt7XVTsjlv6wiJdfeJ4qZxWXj72aG2+5rdb2Lz75iM8++gCHI4CwsDAeffJp2qelUVFRwQv/90e2bN6IMQ7uf+S/6N2vv59q4T1pCS25uGcXjDGs2Z3FD1t319qe0jKGi3t2IaFFJJ8tW8/m7Bw/RXrmdUxsyZhervN/1c69LKxzPQxwGK7ufy5tYltQUl7BR0vWcLS4FIcxXNE3nTaxLbAWpq/ZzO5DeQDcOLQvkaEhOByGzEN5TFu16aztmiU8+QgR5w2iKu8ImTff5e9wpBE4ZSfdGHMf8K61Ns8H8XjEGPjFeX146dv5HCkq5vGxF7I2M5v9RwrcZc7r2p7isnKe+fBb+qW15ZoB5/LG3CUUlpXz6syFHC0upU1sC+4fM5yn3v/avd+/5y0l81CjqeqPYgxcM7Anr83+gaPFJTxw6Qg27N1PztFCd5mBndpRUl7Bc5/PpldqEpf1SefdBSsAOFxYxP/7Zl691317/nLKKioBuHlEf3q2S2LN7mzfVOoM6J+WQlJsC+6cNJWuSQn8esxwHnnrs3rl7h0zjH98+z2bs3P4/fWX0C8thRUZe3ju89nuMrePGkxxWTkAuw/m8uB/PsVpLbERYbz8q+tYsm03zibwDcYYGHdeH16aNp+8omKeuOrE59DvPvyW/mltuWbgubwxZwkVVVV8uWIDSbHRJMW2cJePCAnm2oE9+b/PZlFYWs4tI/rTNSmBLU2oE9K7fRKtY6J4+M3P6dS6FbePGsT/TJ1Wr9yvLhjE5NmL2bb/EI9fNYpeqa5zYl3mPt5fuAqntdwwtA9XDejBewtXMahzKoEBATz+7lcEBwbw/E1jWbhlJ4cKivxQy5+uqqqKF//6Z/76j1eIT0jk7ltv4rzhI2mfluYuc+HoSxh77XUALPx+Hq+8+AJ/efFlvvrsUwD+NeUD8nJzefyh+5n4n7dxOM6eUZcGGNOrK+8tXEV+SSm3XTCQbfsO1fp/zi8p5csVGxncOdV/gXqBAS7t3Z13Fqwgv7iUO0YNZsu+g7Xq3qd9W0oqKnh5+gLOaduai3p04eOla+nboS0Ak2b9QHhIMDcO7cvkOYsB+GjJGsorXYmz6wf3Ir1tazbs3e/z+vlC/jczOPrxFyQ+/Zi/Q2kSmsJn7eny5OrYGlhmjPnAGHOJMcZ4O6hTaR8fx8H8Qg4XFFHltKzI2EOvdkm1yvRsl8Ti7a4MxqqdWXRNSgBg7+EjHC0uBWBfXj6BAQ4Cz5IPiXYtYzlUUERuYTFVTsvqXVmc07Z1rTLntG3Niow9AKzL3Efn1q1O+brHOugOYwhogm01uHN75qzfBsCW7BwiQoKJjQirVSY2IozwkGB3VmvO+m0M6dy+3msN75bGvI2uuxNllVXui0RwYGCTuvV27Bw6VH0OLc/YQ6/U2udQr9QkFm9znUMrd2bRrfocKq+sYseBw1RU33E6plVUBDlHCygsdX2J2ZSdQ5/2yT6ozZnTLy2F+ZsyANi+/xDhIUHEhNc+VmLCwwgLDmLb/kMAzN+UQf+OKYDrnDp2TGzbf4i4yAjXThZCggJxGENwYACVVU5Kyit8VKszZ/PGDSS1TSEpuS1BQUGMung0C7//rlaZiMhI9++lJSUc+8jYvTODvgMGAhAbF0dkVBRbNm30Wey+kBQXTV5RCUeKS3Bay8a9B+jcJr5WmaPFpRzMLzzrHnpLjosmr6iYI0Wuum/Yu9/9uXtM16R41lYneDZmHaBDQhwA8S0i2JnjumNVXFZOWUWFOwFwrIN+7POnKV1nf6zSNeupyi84dUFpNk6ZSbfWPm2M+R9gNHAb8LIx5gPgDWvtDm8H2JCY8DDyikrcy3nFJbSPj6tdJiKMvEJXGae1lJRXEBESTFF1FhSgT/tk9h4+QqXT6V530/D+OK2rgztt9SYv1+TMahEeypHi4+1ytLiUdq1ia5WJrlHGaS2lFZWEhwQDEBcZzkOXjaS0ooLpqzezs8Zt/jtGDSalZQxbsnNYm9l0sugALaPCOVhw/G7CoYIiWkZF1DqGWkZFcLhemfBar3NOSmuOFJWQnZfvXte1TTwPXjaShOgo/vbV3Cbzzb7eOVRUQoe651D4qc+hmg7mF5IYE0VcZDhHikronZpEQEDT+lIXFxnO4cLjmb/cwmLiIsNqnVdxkWHkFha7lw8XFhMXWftYATg/vROLq2/3L9m+m35pKbx6x3UEBwXy9vfLT9iOjdmhnBwSEhPdy/EJiWzaUH/YzqcffsBH771DRUUlL/xzIgAdO3dh4fffMeri0eTkHGDr5k3kHDhA93N6+Cx+b4sKDSG/pNS9XFBSSlJstB8j8p2osFB3AgxcdwyS42rXPSo0lKPV7WOrP3/CgoM4cLSArknxrN+7n+iwUNrEtKBFWKj7Wjt+WF+SYqPZvv8Qm/Ye8F2lpFE7277oNsSjMenWWmuM2Q/sByqBWOAjY8xMa+1/ezNAj/3I/6s2MS24esC5/OPb+e51//5uCUeLSwkJCmTChUMY1KkdS7ZnnuFAvaehWxweHcTWkl9Sxh8/mUlxeQXJcdHcOnIgz381151FnzxnMYEOBzcO60unxHi27T94ZoP3ItNQy9hTl6nbciO7d2Leptpj/LfsO8i9b3xESssYHr78fJbv2FMvw9wYNXQ/rN6R8iPvmRWXV/DewlXcMWow1loycg7TKirip4boFw2eQ/UKNXCs1Cl09YAeOJ1OFmzZCUDHxFY4reXeNz4iIiSE310/mvWZ+8jJL6z3Wo1ZQ1nMhm6uXnP9z7nm+p8za/o03v73ZJ783bNcduVYMnft5K5bbyKxdRt6nNuLgIAAX4Qt/lL3cDnBNWXVrmxaRUVy56hBHC0uZU/ukVoJj3cXrCTA4eDagefSISGOjJz6z4mInI08GZP+AHALcAiYDDxmra0wxjiAbUC9TroxZgIwAWDSpEkQEFe3yGk5UlxSa7hCbHgYR2tkugCOFJUQW50BcxhDWHCQO3MVEx7GhIuG8Oa8ZbXGyx3LApRVVLJsRyap8XFNqpN+tLi01q356PDQWlmdmmWOPawTGhRIcfVt9+Jy1x2FrNyjHC4sIj4qgr25R937VjqdbNh7gHNSWjf6TvrlfdO5pFc3ALbuO0h8VCTgysC0ioqolS0FOFRQSMuo47fpW0VFkFtwPFvqMIbzurbnwf982uD77Tl8hLKKSlLjY9lePQyiMTv2sPAxsRE/7hw6kXWZ+1iXuQ+AYV07NIk7Cxf37MKoHp0ByDhwmJaREYDr+I6LDHffTTgmt6B25rxlZDh5RcePlRHd0+jToS1//GSme93Qrh1YszuLKqclv6SUrdkHSUts2eQ66fEJieQcOJ7JPJhzgJatTjxkbtTFY/j7c/8HQEBgIL9++FH3tvvuuI22Ke28F6wfFJSW0SIs1L0cFRZKQRObfOCnKigpJTr8eN1bNFD3gpJSosNCKSgpw1R//hwb9lXzwfXbzh9Y624VQJXTyZbsg3RJSlAnXZoNT+5FtwKutdaOsdZ+aK2tALDWOoErGtrBWvuatba/tbb/hAkTzmC4LrsP5pHQIpKWkeEEOAz90lJYW90xOGZt5j4Gd3I9mNOnQ7L74bWw4CDuHT2Uz5evJyPn+AwfDmOIqB724TCGHilt2FdjWENTsOfwEVpFRRAb4WqX3u2T2Vjn1uDGvfvpl+YaP3tuuzZsP+DqUEaEBLsThHGR4dUd2WKCAwOICgsBXO3SLSmBnKONf8zc1ys3cv+/P+H+f3/C4m273J2wrkkJFJWV1xrqAa5Oa0l5uXsM5agenVm8bZd7+7GhUYdrfKlLjI7CUd1o8S0iSY6LbhJtA/XPof5pKazdXecc2r3P/XBb3xrn0MlEhbqOlfDgIEakd2Th5p1nPvgzbObarTw55WuenPI1y3fsYXh310OQnVq3orisotZQF3AlCUorKuhU/TzH8O5p7uc8eqUmcWW/c3j+y7nusbTgGj51Torr+ZCQwEA6tW5Fdt5Rmppu3dPJ2rOHfdlZVFRUMGfmDM4bMbJWmb2ZxxMbixcuILm6I15aWkJJiastly9ZTEBAQK0HTs8G2Xn5xEaGER0eisMY0tsmsm1f405onClZefnERYYTEx6GwxjOaduarXWuGVuyD9Kz+tmX9ORE95DKwAAHQdV3VdIS4nA6LYcKiggKCCAy1PW5bIyhc+tWta7B0rw5re9+/MWTMem/Pck2vwzadlrL1B9Wc98lw3EYww9bd7HvSD5X9E1n96E81mXuY9HWndw6ciDPXH8JxWXlvDF3CQAj0zsS3yKSS3t359Le3QHXVIvllZXcf8lwAhwGYwxbsnNYsCXDH9X7yZzW8tmyddx54WDXFIw7MjlwtIDRPbuyN/cIG/ceYOn2TMYN7cvjV11IcVm5e2aXtISW/7+9Ow+TqrrTOP596SagwxaEiRrjEIlrHNERHYmomCEmxhCDS8DggjHjqI9bMsbHLGNaTYzbmBgdjYqocRuDiGMkCoLBsAQQpQElIFFbEc1jUBEXgkuf+eOcoi/V1S1bV93qfj/P00+funXq1jm/PvfUqXPPvc1hA3alMQQaQ2D8nIWsef8DunXtwslD9qe2Uw1SvJiucDFhtXjiueUM3GlHxvzHyHgLxt9PW/fctScfxVm33g/A/0ya7bYTwQAADepJREFUwXePGEKX2lrmPb+ceWngBXDwHv15fPH6l2DsscO2HHvAAD5qbKQxwPWTZ7B6TXXMmjWGwP/Oquesw+MxNCtzDL208k0WvvQqM9MxdFHRMQTw0xGH07VzZ2pqOjGg3/b86uHp/HXV2xw7aAA79O4FwO/nL666meL5DSvYu9+n+eVJ32Dthx9y46Oz1j33828dwQ/ujneCGvvYHE770oHxFowvrqC+IV6nMXrIfnSuqeGHw4cC8Xi55bE5TF64lNO+9AWuPH4YAI8vfo6XVq4qc+02X01tLWefdz7nn30mjY0fcfiwI/nsTv0Ze+MN7Lr7Hhx48CFMGHcvTz4xl9raWrp3784FP7kIgFVvvMn555yJOok+ff+RH9RdUuHabHkhBCYvWMrIA/ehE2LBi6+w8u13OXj3nXj1zdUs++tKtuvVg6MP2IuunTvzue36cNDuO3Hz1NmVLvpmCyHwcP0SRg3+FyRR37CCv739LkP26M8rb67m2Vf/xvyGFQzfb0/O/PJg1rz/AePTbX7/ocsnGDV4X0IIvP33tTwwL9669RO1NYwYtA+1NZ2QRMNrbzDv+ZcrWc02tW3dBWy1917U9OpJv/vv5I1b7mD1xEmVLpZVkMqw8D6ccct9bf0eVeX6U47h+3c+WOli5MqVx38dgCMuu6nCJcmXiRecyuljfPxk3fCdeHu/4665o8IlyZd7zjmBV1ZV15eitrZ9r7iE7dIJUypcknz54fChXDx+cqWLkSsXHn0YywZ/udLFyJWdZ0yCjb46qXyGXzW2bHPcE877dkXiUF23XjAzMzMz6wCq9j+OmpmZmVnH1BFuweiZdDMzMzOznPFMupmZmZlVlWq4xe/m8ky6mZmZmVnOeCbdzMzMzKqKZ9LNzMzMzKzsPJNuZmZmZlXFd3cxMzMzM7Oy80y6mZmZmVWVDjCR7pl0MzMzM7O88Uy6mZmZmVUV393FzMzMzMzKzoN0MzMzM7Oc8XIXMzMzM6sqvgWjmZmZmZmVnWfSzczMzKyq+MJRMzMzMzMrO8+km5mZmVlV8Zp0MzMzMzMrO8+km5mZmVlV6QAT6Z5JNzMzMzPLG8+km5mZmVlV8d1dzMzMzMys7DyTbmZmZmZVpSPc3UVlqGT7j6KZmZlZ+6NKF6AlQ+quK9v4clrdmRWJQzkG6bkh6dQQwk2VLkeeOCalOS7NOSalOS7NOSalOS7NOSbNOSZW0NHWpJ9a6QLkkGNSmuPSnGNSmuPSnGNSmuPSnGPSnGNiQMcbpJuZmZmZ5Z4H6WZmZmZmOdPRBule49WcY1Ka49KcY1Ka49KcY1Ka49KcY9KcY2JAB7tw1MzMzMysGnS0mXQzMzMzs9zzIL1KSeon6emNyH+bpGPaskwdjaSvS7qg0uWoNEm9JJ2ReTxE0kOVLFNbkVQn6bxKl6Otbal6lmgb20u6b3P3m3ft+RhoC5IuljS00uUwyxsP0s02UQjhwRDCZZUuRw70As742FzWLklq7T9Xr9c2QgivhBA8WdCOSarZ2NeEEC4MIUxpi/KYVbN2NUiX9ICkJyU9I+nUtO0USc9KmibpZknXpe19JY2X9ET6ObCypd8ktZJul7RQ0n2StpZ0YarP05JuktTsv2S1lCfF6HJJc1PMDkrbayRdJWlReq+z0vZ9JT2eYj5J0nblrLykE1N5Fki6Q9IwSXMkzZc0RdKnUr66FKfJkhokHSXpilSfRyR1TvkaMvWfK+lzaXtL+x2daU/9Jc1Ocb1Y0jtp+5AU1/skLZF0V6m/SbmkMzBLJI1Jf/+7JA2VNFPSMkn7S+qdjqWFqU57pdfWSRqb6vO8pLPTbi8D+kuql3Rl2tYtL3XeXJJ+JGmppCnArmnbv6e/9YLUj2wtqbukFzLtqUdqU50rWoEN1EI9p0kamNJ9JDWk9GhJ4yT9DpgsqZukqZKeSsfVkWm367UNZc4ASuoq6daUf76kQzP7vj8dm8skXVHmULQqcwzdrvX73q+k7TOAozL595c0K9VxlqRCbKdL2juTb6akvSQdkuJVn17TvQLVLKmVujcofq7MAI5N/eEjip8N0yXtJqlnytcp7WtrScsldVbmTK+kf0v1XpT6my5pe4OkPik9UNK0lM5tvDaWpEsknZN5/LNMP2sdUQih3fwAvdPvrYCngU8DDUBvoDMwHbgu5bkbGJzSOwJ/rnT5N7Ku/YAAHJgejwXOK8QgbbsDGJbStwHHZONUIs804L9T+qvAlJQ+HRgP1BZen+I5C+ibto0Axpax/p8HlgJ9MmX6JE0XQ38nU5c6YEYq8wDgPeDw9NwE4Bsp3QD8KKVPBB5K6Zb2OzrTnh4Cjkvp04B3UnoI8BawA/FL8Z8K7a6C7eZD4J9TeZ5MbUfAkcADwLXAT1L+LwL1mTjOAroAfYDXU0z7AU9n3iNXdd7MeO0LLAK2BnoAf0nH2TaZPD8FzkrpWzPt6dRCW8n7Tyv1nAYMTHn6AA0pPRp4maY+txbokcn3l9SmitvGusfAfwK3pvRuwEtA17Tv54Ge6fGLwGcqHaOiOhT3vT8GlgM7p3r/lqb+owdNfedQYHxKnwT8MqV3Aeal9O8y++5WeG0eflqo+3nEvvP8TL6pwM4p/a/AYyn9f8ChKT0CGJPStwHHpL/3cmCXtP03wLkp3UBTfz8QmJb3eG1ifJ9K6U7Ac2T6Gv90vJ92NZMOnC1pATAb+AxwAvB4COGNEMIHwLhM3qHAdZLqgQeBHlX4DXx5CGFmSt8JDAYOVZz1XUQcYH2+xOtay3N/+v0kscOAGKtfhxA+BAghvEGcadsTeDTF8MfEQVm5fBG4L4SwMlOmHYBJqV7fZ/16PZzawCKgBngkbV9EUz0B7sn8HpTSre23YBBN7evuoufmhhBeDiE0AvVF71cJL4QQFqXyPANMDSEEmmIxmPjljRDCY8A2knqm104MIaxNcX8N+FQL75G3Om+qg4AJIYT3QgiriX0FwJ5phnARMIqmNjEGODmlTyYO2qtBS/VszaPpuIM4ML1U0kJgCnGCpKW2UZBtZ0uIg/Fd0nNTQwhvhRD+DiwG/mmjatP2ivvegcTjalk6lu7M5O0JjEtnEH5BU1sZB3wtnWn5NnGgCjATuDrNoPYq9Ls5UupzB+BeAEndgC8Q61wP3Ahsl8kzIqVHFl6TsSsxjs+mx7cDB39MefIerw0WQmgAXpe0D3AYMD+E8HplS2WV1NpawqoiaQhxMDkohPBeOhW2FNi9hZd0SnnXlKeEbaL4/pkBuJ4487VcUh1xZmIdSV0/Js/a9PsjmtqHSryXgGdCCIOojFJluha4OoTwYGoPdZnn1gKEEBolfZA+SAEaWf84CCXSre13Q6zNpLNxrZRseRozjwuxKPUhV4jFhtYlb3XeHKXuU3sbccZ8gaTRxLMHhBBmpiUBhwA1IYQNvrg7B0rV80OalkV2LXru3Ux6FNAX2DeE8EFaFlOcv1hrS6Dy3n6KY9WzxLaCS4A/hBCGS+pHPDtB+px6lHgG65vEgT4hhMskTSSezZwtaWj6EpMXpT53oKk9dAJWhRD2prkHgZ9L6k08e/NY0fOttYmSbbEK4rWxxhDPJm1LPFNhHVh7mknvCbyZOr7dgAOIp24PkfRJxYubjs7knwycWXiQXRtYRXaUVBgkH0dc0gGwMs1mlLpAq+sG5Ck2GTgtxZDUwS4F+hbeP60rLDXD3FamAt+UtE2mTD2BFen5kzZxvyMyv/+U0huy39k0ta+Rm/jeefFH4qCr8OV3ZZpdbcnbQLWdhdpQfwSGS9oqnWkblrZ3B15Ns6Cjil7zG+KZmGqZRYeW69lAHExB631FT+C1NEA/lKaZ79baRrad7UJcdrh0k2tQXsV97xTgs5L6Z7YVZPuP0UX7GQP8CniicFZCUv90putyYB5xKVCetPS5A0DqK16QdCyAogHpuXeAucA1xOVAHxXtewnQT+l6INLZ8JRuoKktrvssr4J4bawJwFeA/YBJFS6LVVh7GqQ/QryQciFx5mI2sWO8FJhD7EQXE9fKApwNDEwXvywmriOuNn8GTkp17g3cANxMXLbwAPBE8QtCCKs+Lk8JY4jrRRem5UTfCiG8T/zQvjxtqyee4iyLEMIzwM+Ax9P7X02c4R4naTqwchN33UXSHOAc4Ltp24bs91zge5LmEk/tvtVCvmpQRzo2iBf+tfqFJ52Onal4IeqVreWtNiGEp4in5OuJ12VMT0/9F7FfeZQ4sMi6i3gdwz1UiVbqeRVwuqRZxLXmLbmL2GbmEQfeS9J+W2sb1wM1acnQvcDoEMJaqkNx3/sL4jUIExUvnnwxk/cK4uzxTOJSu3VCCE8Cq1n/C925KV4LgDXAw21XjU1S6nOn2CjglFSHZ4hnCwruBY6n+VIX0vKmk4n97SLi2b1fp6cvAq5J/XB2cJ/3eG2U9Nn6B+C3Jb7EWAfT7v/jqKRuIYR30izwBOLFjRMqXS7Ln3SKfmBhnftGvnZrYE0IIUgaSbyI9MiPe521P4p3qTgyhHBCpctiW15asvJQCGHPLbCv7YnLX3ZL12/k2pasu5WmePebp4BjQwjLKl0eq6y8rfNrC3WK/yShK3HZxgMVLo+1T/sSL0QWsIp4IZh1MJKuBQ4nro81a5GkE4lnA79XDQN0a3uS9iDeKWyCB+gGHWAm3czMzMys2rSnNelmZmZmZu2CB+lmZmZmZjnjQbqZmZmZWc54kG5mZmZmljMepJuZmZmZ5YwH6WZmZmZmOfP/CsbvMwJZ6h4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a0a89240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_features = ['age', 'balance', 'campaign', 'month', 'day', 'duration', 'pdays', 'previous']\n",
    "numerical_features_y = numerical_features + ['y']\n",
    "plt.figure(figsize=(14,10))\n",
    "cmap = sb.diverging_palette(220, 10, as_cmap=True)\n",
    "sb.heatmap(bank[numerical_features_y].corr(), cmap=cmap, annot=True, linewidths=.5);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration    0.394521\n",
       "pdays       0.103621\n",
       "previous    0.093236\n",
       "balance     0.052838\n",
       "age         0.025155\n",
       "month       0.018717\n",
       "day        -0.028348\n",
       "campaign   -0.073172\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank[numerical_features].corrwith(bank['y']).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encode categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank = pd.get_dummies(bank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>...</th>\n",
       "      <th>education_secondary</th>\n",
       "      <th>education_tertiary</th>\n",
       "      <th>education_unknown</th>\n",
       "      <th>contact_cellular</th>\n",
       "      <th>contact_telephone</th>\n",
       "      <th>contact_unknown</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_other</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>poutcome_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>2143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  default  balance  housing  loan  day  month  duration  campaign  \\\n",
       "0   58        0     2143        1     0    5      5       261         1   \n",
       "1   44        0       29        1     0    5      5       151         1   \n",
       "2   33        0        2        1     1    5      5        76         1   \n",
       "3   47        0     1506        1     0    5      5        92         1   \n",
       "4   33        0        1        0     0    5      5       198         1   \n",
       "\n",
       "   pdays        ...         education_secondary  education_tertiary  \\\n",
       "0     -1        ...                           0                   1   \n",
       "1     -1        ...                           1                   0   \n",
       "2     -1        ...                           1                   0   \n",
       "3     -1        ...                           0                   0   \n",
       "4     -1        ...                           0                   0   \n",
       "\n",
       "   education_unknown  contact_cellular  contact_telephone  contact_unknown  \\\n",
       "0                  0                 0                  0                1   \n",
       "1                  0                 0                  0                1   \n",
       "2                  0                 0                  0                1   \n",
       "3                  1                 0                  0                1   \n",
       "4                  1                 0                  0                1   \n",
       "\n",
       "   poutcome_failure  poutcome_other  poutcome_success  poutcome_unknown  \n",
       "0                 0               0                 0                 1  \n",
       "1                 0               0                 0                 1  \n",
       "2                 0               0                 0                 1  \n",
       "3                 0               0                 0                 1  \n",
       "4                 0               0                 0                 1  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train_test():\n",
    "    features = bank.columns.tolist()\n",
    "    features.remove('y')\n",
    "    X = bank[features]\n",
    "    y = bank['y']\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25, stratify=y, random_state=42)\n",
    "    X_train = pd.DataFrame(X_train, columns=X.columns)\n",
    "    y_train = pd.DataFrame(y_train, columns=['y'])\n",
    "    X_test = pd.DataFrame(X_test, columns=X.columns)\n",
    "    y_test = pd.DataFrame(y_test, columns=['y'])\n",
    "    train = pd.concat((X_train, y_train), axis=1)\n",
    "    test = pd.concat((X_test, y_test), axis=1)\n",
    "    return train, test\n",
    "    \n",
    "train, test = make_train_test()\n",
    "features = train.columns.tolist()\n",
    "features.remove('y')\n",
    "#features.remove('duration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_f1_test(model, features):\n",
    "    model.fit(train[features], train['y'].values)\n",
    "    y_pred_test = model.predict(test[features])\n",
    "    return f1_score(y_pred=y_pred_test, y_true=test['y']) \n",
    "\n",
    "def evaluate_model(model, features):\n",
    "    model.fit(train[features], train['y'].values)\n",
    "    y_pred_train = model.predict(train[features])\n",
    "    y_pred_test = model.predict(test[features])\n",
    "\n",
    "    accuracy_train = accuracy_score(y_pred=y_pred_train, y_true=train['y'])\n",
    "    precision_train = precision_score(y_pred=y_pred_train, y_true=train['y'])\n",
    "    recall_train = recall_score(y_pred=y_pred_train, y_true=train['y'])\n",
    "    f1_train = f1_score(y_pred=y_pred_train, y_true=train['y'])\n",
    "\n",
    "    accuracy_test = accuracy_score(y_pred=y_pred_test, y_true=test['y'])\n",
    "    precision_test = precision_score(y_pred=y_pred_test, y_true=test['y'])\n",
    "    recall_test = recall_score(y_pred=y_pred_test, y_true=test['y'])\n",
    "    f1_test = f1_score(y_pred=y_pred_test, y_true=test['y'])\n",
    "\n",
    "    print('TRAIN: precision: ' + str(round(precision_train,3)) + \n",
    "          ', recall: ' + str(round(recall_train, 3)) + ', f1: ' + str(round(f1_train, 3)) )\n",
    "    print('TEST:  precision: ' + str(round(precision_test,3)) +\n",
    "          ', recall: ' + str(round(recall_test, 3)) + ', f1: ' + str(round(f1_test, 3)) )\n",
    "    \n",
    "    #print('TRAIN: accuracy: ' + str(round(accuracy_train,3)) + ', precision: ' + str(round(precision_train,3)) + \n",
    "    #      ', recall: ' + str(round(recall_train, 3)) + ', f1: ' + str(round(f1_train, 3)) )\n",
    "    #print('TEST:  accuracy: ' + str(round(accuracy_test,3)) + ', precision: ' + str(round(precision_test,3)) +\n",
    "    #      ', recall: ' + str(round(recall_test, 3)) + ', f1: ' + str(round(f1_test, 3)) )\n",
    "    print()\n",
    "    print('Confusion Matrix Test Set')\n",
    "    print(pd.crosstab(test['y'], y_pred_test, rownames=['actual'], colnames=['preds']))\n",
    "    print()\n",
    "    \n",
    "    #fpr, tpr, _ = roc_curve(test['y'], y_pred_test)\n",
    "    #roc_auc = auc(fpr, tpr)\n",
    "    #print('AUC: ' + str(roc_auc))\n",
    "    #print()\n",
    "    \n",
    "def get_features_importances(model, features=features):\n",
    "    print('Features Importance')\n",
    "    idx = reversed(np.argsort(model.feature_importances_))\n",
    "    for i in idx:\n",
    "        importance = round(model.feature_importances_[i], 3)\n",
    "        if importance > 0.01:\n",
    "            print(train.columns[i] + ': ' + str(importance))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a dummy classifier that randomly generates outputs with the same probability distribution as the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 0.117, recall: 0.116, f1: 0.117\n",
      "TEST:  precision: 0.109, recall: 0.105, f1: 0.107\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0     1\n",
      "actual            \n",
      "0       8845  1136\n",
      "1       1183   139\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dummy_stratified = DummyClassifier(strategy='stratified', random_state=42)\n",
    "evaluate_model(dummy_stratified, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 1.0, recall: 1.0, f1: 1.0\n",
      "TEST:  precision: 0.479, recall: 0.477, f1: 0.478\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0    1\n",
      "actual           \n",
      "0       9295  686\n",
      "1        692  630\n",
      "\n",
      "Features Importance\n",
      "duration: 0.282\n",
      "balance: 0.109\n",
      "day: 0.092\n",
      "poutcome_success: 0.089\n",
      "age: 0.089\n",
      "month: 0.079\n",
      "pdays: 0.045\n",
      "campaign: 0.035\n",
      "housing: 0.017\n",
      "contact_unknown: 0.013\n",
      "previous: 0.012\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(random_state=42)\n",
    "evaluate_model(tree, features)\n",
    "get_features_importances(tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If not constrained (regularized) the tree will overfit the data\n",
    "This you can notice because of the perfect TRAIN performance and poor (but much better than baseline) TEST performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 0.675, recall: 0.35, f1: 0.461\n",
      "TEST:  precision: 0.636, recall: 0.331, f1: 0.436\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0    1\n",
      "actual           \n",
      "0       9730  251\n",
      "1        884  438\n",
      "\n",
      "Features Importance\n",
      "duration: 0.578\n",
      "poutcome_success: 0.294\n",
      "age: 0.051\n",
      "contact_unknown: 0.04\n",
      "month: 0.018\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=5, random_state=42)\n",
    "evaluate_model(tree, features)\n",
    "get_features_importances(tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's find the best depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_f1_max_depth(max_depth, features=features, class_weight=None):\n",
    "    f1s = []\n",
    "    for depth in range(2,max_depth):\n",
    "        tree = DecisionTreeClassifier(max_depth=depth, random_state=42, class_weight=class_weight)\n",
    "        f1s.append(get_f1_test(tree, features))\n",
    "    plt.plot(range(2,max_depth), f1s, label='F1 score testing')\n",
    "    plt.title('F1 score as max_depth varies')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    print('best: max_depth=' + str(2 + np.argmax(f1s)) + ', f1: ' + str(np.max(f1s)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a14d8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best: max_depth=13, f1: 0.5142385472554685\n"
     ]
    }
   ],
   "source": [
    "get_f1_max_depth(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 0.841, recall: 0.699, f1: 0.763\n",
      "TEST:  precision: 0.566, recall: 0.471, f1: 0.514\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0    1\n",
      "actual           \n",
      "0       9503  478\n",
      "1        699  623\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=13, random_state=42)\n",
    "evaluate_model(tree, features)\n",
    "#get_features_importances(tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's account for the fact we have an imlanced output (after all, most people decide not to subscribe the deposit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can do this by adding *class_weight='balanced'*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 0.523, recall: 0.963, f1: 0.678\n",
      "TEST:  precision: 0.417, recall: 0.761, f1: 0.539\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0     1\n",
      "actual            \n",
      "0       8573  1408\n",
      "1        316  1006\n",
      "\n",
      "Features Importance\n",
      "duration: 0.392\n",
      "month: 0.132\n",
      "poutcome_success: 0.111\n",
      "day: 0.088\n",
      "contact_unknown: 0.071\n",
      "housing: 0.044\n",
      "balance: 0.037\n",
      "age: 0.034\n",
      "pdays: 0.021\n",
      "campaign: 0.012\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=13, class_weight='balanced', random_state=42)\n",
    "evaluate_model(tree, features)\n",
    "get_features_importances(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a1fc4390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best: max_depth=13, f1: 0.5385438972162742\n"
     ]
    }
   ],
   "source": [
    "get_f1_max_depth(40, class_weight='balanced')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 0.596, recall: 0.943, f1: 0.73\n",
      "TEST:  precision: 0.478, recall: 0.75, f1: 0.584\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0     1\n",
      "actual            \n",
      "0       8898  1083\n",
      "1        330   992\n",
      "\n",
      "Features Importance\n",
      "duration: 0.417\n",
      "month: 0.073\n",
      "age: 0.056\n",
      "balance: 0.054\n",
      "day: 0.051\n",
      "poutcome_success: 0.05\n",
      "pdays: 0.038\n",
      "contact_unknown: 0.037\n",
      "housing: 0.034\n",
      "contact_cellular: 0.026\n",
      "previous: 0.024\n",
      "campaign: 0.023\n",
      "poutcome_unknown: 0.012\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(max_depth=13, class_weight='balanced', n_estimators=50, random_state=42)\n",
    "evaluate_model(rf, features)\n",
    "get_features_importances(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's inspect a smaller tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 0.317, recall: 0.802, f1: 0.454\n",
      "TEST:  precision: 0.309, recall: 0.784, f1: 0.443\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0     1\n",
      "actual            \n",
      "0       7665  2316\n",
      "1        286  1036\n",
      "\n",
      "Features Importance\n",
      "duration: 0.641\n",
      "poutcome_success: 0.176\n",
      "contact_unknown: 0.111\n",
      "month: 0.071\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=3, class_weight='balanced', random_state=42)\n",
    "evaluate_model(tree, features)\n",
    "get_features_importances(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(tree, out_file =\"bank.dot\", feature_names = features, class_names=['no','yes'], rounded = True, filled = True, proportion=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Decision Tree with max_depth=3](img/bank_tree.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We just need to keep the conversation going for as long as possible ...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**But** Correlation does not implies Causation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![But Correlation is not Causation](img/correlation_not_causation.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Image Credit: xkcd - https://xkcd.com/552"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Even if correlation does not imply causation ... it doesn't hurt not being the first saying goodbye"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We cannot conclude yet !\n",
    "### We got some insights, but if we really want to predict, we cannot use data from *this* campaign\n",
    "### Let's restrict ourselves to make use of previous campaign data only\n",
    "This means that we cannot use the following features:\n",
    "* contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") \n",
    "* day: last contact day of the month (numeric)\n",
    "* month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n",
    "* **duration**: last contact duration, in seconds (numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Take out the features about this campaign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "contact_features = [f for f in features if f.startswith('contact')]\n",
    "new_campaign_features = features.copy()\n",
    "for f in contact_features:\n",
    "    new_campaign_features.remove(f)\n",
    "new_campaign_features.remove('day')\n",
    "new_campaign_features.remove('month')\n",
    "new_campaign_features.remove('duration')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 0.324, recall: 0.675, f1: 0.437\n",
      "TEST:  precision: 0.241, recall: 0.509, f1: 0.327\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0     1\n",
      "actual            \n",
      "0       7864  2117\n",
      "1        649   673\n",
      "\n",
      "Features Importance\n",
      "education_unknown: 0.24\n",
      "balance: 0.195\n",
      "age: 0.14\n",
      "housing: 0.096\n",
      "month: 0.087\n",
      "day: 0.052\n",
      "duration: 0.026\n",
      "job_unemployed: 0.019\n",
      "loan: 0.016\n",
      "pdays: 0.014\n",
      "marital_divorced: 0.013\n",
      "marital_single: 0.012\n",
      "job_management: 0.011\n",
      "job_blue-collar: 0.011\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=13, class_weight='balanced', random_state=42)\n",
    "evaluate_model(tree, new_campaign_features)\n",
    "get_features_importances(tree, features=new_campaign_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the optimal depth for maximizing F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286a1fd1898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best: max_depth=6, f1: 0.3984063745019921\n"
     ]
    }
   ],
   "source": [
    "get_f1_max_depth(40, features=new_campaign_features, class_weight='balanced')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the tree with the optimal depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 0.382, recall: 0.421, f1: 0.401\n",
      "TEST:  precision: 0.382, recall: 0.416, f1: 0.398\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0    1\n",
      "actual           \n",
      "0       9092  889\n",
      "1        772  550\n",
      "\n",
      "Features Importance\n",
      "education_unknown: 0.445\n",
      "housing: 0.177\n",
      "age: 0.144\n",
      "balance: 0.097\n",
      "month: 0.087\n",
      "job_unemployed: 0.026\n",
      "day: 0.013\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=6, class_weight='balanced', random_state=42)\n",
    "evaluate_model(tree, new_campaign_features)\n",
    "get_features_importances(tree, features=new_campaign_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the random forests with the optimal depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: precision: 0.304, recall: 0.537, f1: 0.388\n",
      "TEST:  precision: 0.295, recall: 0.523, f1: 0.378\n",
      "\n",
      "Confusion Matrix Test Set\n",
      "preds      0     1\n",
      "actual            \n",
      "0       8329  1652\n",
      "1        630   692\n",
      "\n",
      "Features Importance\n",
      "education_unknown: 0.196\n",
      "housing: 0.13\n",
      "month: 0.117\n",
      "age: 0.108\n",
      "balance: 0.08\n",
      "contact_cellular: 0.079\n",
      "duration: 0.067\n",
      "day: 0.036\n",
      "education_secondary: 0.032\n",
      "job_unemployed: 0.027\n",
      "loan: 0.022\n",
      "job_unknown: 0.018\n",
      "pdays: 0.016\n",
      "job_retired: 0.016\n",
      "job_entrepreneur: 0.015\n",
      "marital_single: 0.011\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(max_depth=6, class_weight='balanced', n_estimators=50, random_state=42)\n",
    "evaluate_model(rf, new_campaign_features)\n",
    "get_features_importances(rf, features=new_campaign_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A word about using validation set ... if we still have time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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